{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e220f96e-71d4-423a-ab8e-e42a2ddabe3f",
   "metadata": {},
   "source": [
    "# Index Tracking with Gurobi"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "dc494c8a",
   "metadata": {},
   "source": [
    "This Python notebook is part of the webinar [Proven Techniques for Solving Financial Problems with Gurobi](https://www.gurobi.com/events/proven-techniques-for-solving-financial-problems-with-gurobi/).\n",
    "\n",
    "The sequence of python code will:\n",
    "1. Import stock data from yahoo finance\n",
    "2. Clean up the data and change format\n",
    "3. Perform an index tracking experiment"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "fc166daa",
   "metadata": {},
   "source": [
    "## Importing Data from YFinance\n",
    "\n",
    "- Adjusted Stock price data for SP100 constitutents \n",
    "- Data from 2010 to 2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a8040b70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching SP100 components\n",
      "\t-> got 101 tickers\n",
      "\n",
      "Available Tickers for ^SP100 at 2023-02-23\n",
      "['AAPL', 'ABBV', 'ABT', 'ACN', 'ADBE', 'AIG', 'AMD', 'AMGN', 'AMT', 'AMZN', 'AVGO', 'AXP', 'BA', 'BAC', 'BK', 'BKNG', 'BLK', 'BMY', 'BRK.B', 'C', 'CAT', 'CHTR', 'CL', 'CMCSA', 'COF', 'COP', 'COST', 'CRM', 'CSCO', 'CVS', 'CVX', 'DHR', 'DIS', 'DOW', 'DUK', 'EMR', 'EXC', 'F', 'FDX', 'GD', 'GE', 'GILD', 'GM', 'GOOG', 'GOOGL', 'GS', 'HD', 'HON', 'IBM', 'INTC', 'JNJ', 'JPM', 'KHC', 'KO', 'LIN', 'LLY', 'LMT', 'LOW', 'MA', 'MCD', 'MDLZ', 'MDT', 'MET', 'META', 'MMM', 'MO', 'MRK', 'MS', 'MSFT', 'NEE', 'NFLX', 'NKE', 'NVDA', 'ORCL', 'PEP', 'PFE', 'PG', 'PM', 'PYPL', 'QCOM', 'RTX', 'SBUX', 'SCHW', 'SO', 'SPG', 'T', 'TGT', 'TMO', 'TMUS', 'TSLA', 'TXN', 'UNH', 'UNP', 'UPS', 'USB', 'V', 'VZ', 'WBA', 'WFC', 'WMT', 'XOM', '^SP100']\n",
      " \n",
      "\n",
      "\n",
      "Original price data\n",
      "            Adj Close ticker\n",
      "Date                        \n",
      "2015-01-02  24.565695   AAPL\n",
      "2015-01-05  23.873644   AAPL\n",
      "2015-01-06  23.875889   AAPL\n",
      "2015-01-07  24.210676   AAPL\n",
      "2015-01-08  25.140911   AAPL\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from utils.data_import import get_mkt_constitution, get_yf_data\n",
    "import os\n",
    "from datetime import datetime\n",
    "\n",
    "# Options\n",
    "FIRST_DATE  = \"2015-01-01\"\n",
    "LAST_DATE   = \"2022-01-01\"\n",
    "N_PROCESSES = 10\n",
    "MKT_INDEX   = \"^SP100\" # ^GSPC for SP500 or ^SP100 \n",
    "#MKT_INDEX   = \"^GSPC\"\n",
    "\n",
    "if not os.path.exists(\"data\"):\n",
    "    os.mkdir(\"data\")\n",
    "    \n",
    "# get mkt constitutents    \n",
    "tickers = get_mkt_constitution(MKT_INDEX)\n",
    "\n",
    "today = datetime.today().strftime('%Y-%m-%d')\n",
    "print(f\"Available Tickers for {MKT_INDEX} at {today}\")\n",
    "print(tickers)\n",
    "print(\" \")\n",
    "\n",
    "df_prices = get_yf_data(tickers, \n",
    "                        FIRST_DATE,\n",
    "                        LAST_DATE,\n",
    "                        N_PROCESSES)\n",
    "\n",
    "print(\"\\n\\nOriginal price data\")\n",
    "print(df_prices.head())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b4ca1f74",
   "metadata": {},
   "source": [
    "## Cleaning and Splitting the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "029a358e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 176752 entries, 2015-01-02 to 2021-12-31\n",
      "Data columns (total 2 columns):\n",
      " #   Column     Non-Null Count   Dtype  \n",
      "---  ------     --------------   -----  \n",
      " 0   Adj Close  176752 non-null  float64\n",
      " 1   ticker     176752 non-null  object \n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 4.0+ MB\n",
      "None\n",
      "Size original: (176752, 2)\n",
      "Size reduced: (172774, 2)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "import numpy  as np\n",
    "import matplotlib.pyplot as plt\n",
    "from utils.data_clean import clean_data\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "THRESH_VALID_DATA = 0.95 # defines where to cut stocks with missing data\n",
    "PERC_SIZE_TRAIN = 0.75   # defines the size of train dataset (in %)\n",
    "\n",
    "df_ret, df_train, df_test  = clean_data(\n",
    "    df_prices, \n",
    "    MKT_INDEX,\n",
    "    thresh_valid_data = THRESH_VALID_DATA,\n",
    "    size_train = PERC_SIZE_TRAIN\n",
    ")\n",
    "\n",
    "df_train.to_parquet(\"data/ret-data-cleaned-TRAIN.parquet\")\n",
    "df_test.to_parquet(\"data/ret-data-cleaned-TEST.parquet\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9b9ff5bc-99e7-40ca-b7d8-fa567f100c7f",
   "metadata": {},
   "source": [
    "## Unconstrained Index Tracking\n",
    "\n",
    "$\n",
    "\\begin{array}{llll}\n",
    "  & \\min              & \\frac{1}{T} \\; \\sum_{t = 1}^{T} \\left(\\sum_{i = 1}^{I} \\; w_{i} \\: \\times \\: r_{i,t} - R_{t}\\right)^2 \\\\\n",
    "  & \\text{subject to} &   \\sum_{i = 1}^{I} w_{i}  = 1  \\\\\n",
    "  &                   & w_i \\geq 0 \\\\\n",
    "\\end{array}\n",
    "$\n",
    "\n",
    "\n",
    "\n",
    "$\n",
    "\\begin{array}{lll}\n",
    "& where: \\\\\n",
    "& \\\\\n",
    "& w_i  &: \\text{Weight of asset i in index} \\\\\n",
    "& R_{t} &: \\text{Returns of tracked index (e.g. SP500) at time t} \\\\\n",
    "& r_{i,j} &: \\text{Return of asset i at time t}\n",
    "\\end{array}\n",
    "$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "61f15803-1ff5-4008-b5be-d673c8b06964",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ticker            SO       ABT       AMD        MS        MA      INTC  \\\n",
      "Date                                                                     \n",
      "2015-01-05 -0.004253  0.000222 -0.003745 -0.031258 -0.028128 -0.011276   \n",
      "2015-01-06  0.012001 -0.011356 -0.011278 -0.028800 -0.002162 -0.018637   \n",
      "2015-01-07  0.011055  0.008108 -0.019011  0.014277  0.015555  0.020975   \n",
      "2015-01-08  0.000796  0.020554  0.011628  0.014889  0.015555  0.018601   \n",
      "2015-01-09 -0.012714 -0.010508  0.007663 -0.016271 -0.012744  0.001908   \n",
      "\n",
      "ticker           SPG        GS       LLY      MDLZ        PG       TMO  \\\n",
      "Date                                                                     \n",
      "2015-01-05  0.004101 -0.031223 -0.009848 -0.014950 -0.004755 -0.013737   \n",
      "2015-01-06  0.027834 -0.020229  0.005046 -0.006898 -0.004555 -0.009339   \n",
      "2015-01-07  0.018402  0.014902 -0.007028  0.031397  0.005245  0.029957   \n",
      "2015-01-08 -0.001438  0.015966  0.023689  0.012662  0.011436  0.010393   \n",
      "2015-01-09  0.010539 -0.015347 -0.013263 -0.009843 -0.009331 -0.003992   \n",
      "\n",
      "ticker           USB       FDX        MO      AVGO       BLK       HON  \\\n",
      "Date                                                                     \n",
      "2015-01-05 -0.024091 -0.015367 -0.005718 -0.015986 -0.025874 -0.019056   \n",
      "2015-01-06 -0.013257 -0.000058  0.005956 -0.022743 -0.015539 -0.002339   \n",
      "2015-01-07  0.008803  0.007362  0.018375  0.027013  0.021163  0.007238   \n",
      "2015-01-08  0.007348  0.023386  0.016841  0.049975  0.011679  0.018219   \n",
      "2015-01-09 -0.020743 -0.013597 -0.002366  0.010695 -0.011856 -0.016600   \n",
      "\n",
      "ticker            GM       BAC  \n",
      "Date                            \n",
      "2015-01-05 -0.014638 -0.029050  \n",
      "2015-01-06  0.015147 -0.029919  \n",
      "2015-01-07  0.028407  0.004745  \n",
      "2015-01-08  0.010045  0.020661  \n",
      "2015-01-09 -0.016851 -0.017929  \n"
     ]
    }
   ],
   "source": [
    "import gurobipy as gp\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from random import sample, seed\n",
    "\n",
    "seed(20220209) # reproducibility\n",
    "\n",
    "mkt_index = \"^SP100\"\n",
    "n_assets = 20\n",
    "\n",
    "# data from main notebook\n",
    "r_it = pd.read_parquet(\"data/ret-data-cleaned-TRAIN.parquet\")\n",
    "\n",
    "r_mkt = r_it[mkt_index]\n",
    "\n",
    "r_it = r_it.drop(mkt_index, axis = 1)\n",
    "\n",
    "tickers = list(r_it.columns)\n",
    "\n",
    "sampled_tickers = sample(tickers, n_assets)\n",
    "\n",
    "r_it = r_it[sampled_tickers]\n",
    "\n",
    "print(r_it.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a033a00-9402-4833-8782-aa31a36f76ad",
   "metadata": {},
   "source": [
    "# Setup opt problem and solve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cac2d632-8e73-4ccd-82f6-064c0f932e3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solution:\n",
      "SO:\t 3.69%\n",
      "ABT:\t 4.85%\n",
      "AMD:\t 0.43%\n",
      "MS:\t 0.00%\n",
      "MA:\t 13.00%\n",
      "INTC:\t 7.69%\n",
      "SPG:\t 2.79%\n",
      "GS:\t 7.41%\n",
      "LLY:\t 6.82%\n",
      "MDLZ:\t 3.44%\n",
      "PG:\t 10.72%\n",
      "TMO:\t 6.61%\n",
      "USB:\t 4.29%\n",
      "FDX:\t 5.46%\n",
      "MO:\t 4.48%\n",
      "AVGO:\t 2.90%\n",
      "BLK:\t 2.22%\n",
      "HON:\t 6.98%\n",
      "GM:\t 2.83%\n",
      "BAC:\t 3.39%\n",
      "\n",
      "checking constraints:\n",
      "sum(w) = 0.9999999999999842\n"
     ]
    }
   ],
   "source": [
    "# Create an empty model\n",
    "m = gp.Model('gurobi_index_tracking')\n",
    "\n",
    "# PARAMETERS \n",
    "\n",
    "# w_i: the i_th stock gets a weight w_i\n",
    "w = pd.Series(m.addVars(sampled_tickers, \n",
    "                         lb = 0,\n",
    "                         ub = 1,\n",
    "                         vtype = gp.GRB.CONTINUOUS), \n",
    "               index=sampled_tickers)\n",
    "\n",
    "# CONSTRAINTS\n",
    "\n",
    "# sum(w_i) = 1: portfolio budget constrain (long only)\n",
    "m.addConstr(w.sum() == 1, 'port_budget')\n",
    "\n",
    "m.update()\n",
    "\n",
    "# eps_t = R_{i,t}*w - R_{M,t}\n",
    "my_error = r_it.dot(w) - r_mkt\n",
    "\n",
    "# set objective function\n",
    "m.setObjective(\n",
    "    gp.quicksum(my_error.pow(2)), \n",
    "    gp.GRB.MINIMIZE)     \n",
    "\n",
    "# Optimize model\n",
    "m.setParam('OutputFlag', 0)\n",
    "m.optimize()\n",
    "\n",
    "w_hat  = [i.X for i in m.getVars()]\n",
    "\n",
    "print(f\"Solution:\") \n",
    "\n",
    "for i, i_ticker in enumerate(sampled_tickers):\n",
    "    print(f\"{i_ticker}:\\t {w_hat[i]*100:.2f}%\")\n",
    "\n",
    "# check constraints\n",
    "print(f\"\\nchecking constraints:\")\n",
    "print(f\"sum(w) = {np.sum(w_hat)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4045ebc9-b04d-4b54-a888-041c4e4b8092",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['AAPL', 'ABBV', 'ABT', 'ACN', 'ADBE', 'AIG', 'AMD', 'AMGN', 'AMT',\n",
      "       'AMZN', 'AVGO', 'AXP', 'BA', 'BAC', 'BK', 'BKNG', 'BLK', 'BMY', 'C',\n",
      "       'CAT', 'CHTR', 'CL', 'CMCSA', 'COF', 'COP', 'COST', 'CRM', 'CSCO',\n",
      "       'CVS', 'CVX', 'DHR', 'DIS', 'DUK', 'EMR', 'EXC', 'F', 'FDX', 'GD', 'GE',\n",
      "       'GILD', 'GM', 'GOOG', 'GOOGL', 'GS', 'HD', 'HON', 'IBM', 'INTC', 'JNJ',\n",
      "       'JPM', 'KO', 'LIN', 'LLY', 'LMT', 'LOW', 'MA', 'MCD', 'MDLZ', 'MDT',\n",
      "       'MET', 'META', 'MMM', 'MO', 'MRK', 'MS', 'MSFT', 'NEE', 'NFLX', 'NKE',\n",
      "       'NVDA', 'ORCL', 'PEP', 'PFE', 'PG', 'PM', 'QCOM', 'RTX', 'SBUX', 'SCHW',\n",
      "       'SO', 'SPG', 'T', 'TGT', 'TMO', 'TMUS', 'TSLA', 'TXN', 'UNH', 'UNP',\n",
      "       'UPS', 'USB', 'V', 'VZ', 'WBA', 'WFC', 'WMT', 'XOM', '^SP100'],\n",
      "      dtype='object', name='ticker')\n",
      "['SO', 'ABT', 'AMD', 'MS', 'MA', 'INTC', 'SPG', 'GS', 'LLY', 'MDLZ', 'PG', 'TMO', 'USB', 'FDX', 'MO', 'AVGO', 'BLK', 'HON', 'GM', 'BAC']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check out of sample plot\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df_test = pd.read_parquet(\"data/ret-data-cleaned-TEST.parquet\")\n",
    "\n",
    "print(df_test.columns)\n",
    "print(sampled_tickers)\n",
    "df_test_mkt = df_test[mkt_index]\n",
    "\n",
    "r_hat = df_test[sampled_tickers].dot(w_hat)\n",
    "\n",
    "cumret_r = np.cumprod(1+ r_hat)\n",
    "cumret_mkt = np.cumprod(1+ df_test_mkt)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(cumret_mkt.index,\n",
    "        cumret_mkt, \n",
    "       label = mkt_index)\n",
    "\n",
    "ax.plot(cumret_r.index,\n",
    "        cumret_r,\n",
    "       label = f\"ETF ({n_assets} assets)\")\n",
    "\n",
    "ax.legend()\n",
    "ax.set_title(f'ETF and {mkt_index}')\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('Cumulative Returns')\n",
    "\n",
    "plt.xticks(rotation = 90)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e3239ca8-ea6d-40e5-854f-a21bd517c7c1",
   "metadata": {},
   "source": [
    "## Constrained Index Tracking\n",
    "\n",
    "$\n",
    "\\begin{array}{llll}\n",
    "  & \\min              & \\frac{1}{T} \\; \\sum_{t = 1}^{T} \\left(\\sum_{i = 1}^{I} \\; w_{i} \\: \\times \\: r_{i,t} - R_{t}\\right)^2 \\\\\n",
    "  & \\text{subject to} &   \\sum_{i = 1}^{I} w_{i}  = 1  \\\\\n",
    "  &                   &   \\sum_{i = 1}^{I} z_{i} \\leq K \\\\\n",
    "  &                   & w_i \\geq 0 \\\\\n",
    "  &                   & z_i \\in {0, 1}\n",
    "\\end{array}\n",
    "$\n",
    "\n",
    "  \n",
    "\n",
    "$\n",
    "\\begin{array}{lllll}\n",
    "& where: \\\\\n",
    "& \\\\\n",
    "& w_i  &: \\text{Weight of asset i in index} \\\\\n",
    "& z_i &: \\text{Binary variable (0, 1) that decides wheter asset i is in portfolio} \\\\\n",
    "& R_{t} &: \\text{Returns of tracked index (e.g. SP500) at time t} \\\\\n",
    "& r_{i,j} &: \\text{Return of asset i at time t}\n",
    "\\end{array}\n",
    "$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "40631468-36d9-41aa-a684-6f103a95340b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solution for w:\n",
      "SO:\t 6.86%\n",
      "ABT:\t 0.00%\n",
      "AMD:\t 0.00%\n",
      "MS:\t 0.00%\n",
      "MA:\t 14.92%\n",
      "INTC:\t 7.93%\n",
      "SPG:\t 0.00%\n",
      "GS:\t 14.51%\n",
      "LLY:\t 8.73%\n",
      "MDLZ:\t 0.00%\n",
      "PG:\t 14.13%\n",
      "TMO:\t 8.72%\n",
      "USB:\t 0.00%\n",
      "FDX:\t 7.64%\n",
      "MO:\t 0.00%\n",
      "AVGO:\t 3.98%\n",
      "BLK:\t 0.00%\n",
      "HON:\t 12.57%\n",
      "GM:\t 0.00%\n",
      "BAC:\t 0.00%\n",
      "\n",
      "checking constraints:\n",
      "sum(w) = 0.9999999999999998\n",
      "sum(z) = 10.0\n",
      "w <= z = True\n",
      "MIPGap=0.0\n",
      "Status=2\n"
     ]
    }
   ],
   "source": [
    "# Create an empty model\n",
    "m = gp.Model('gurobi_index_tracking')\n",
    "\n",
    "# PARAMETERS \n",
    "\n",
    "max_assets = 10\n",
    "\n",
    "# w_i: the i_th stock gets a weight w_i\n",
    "w = pd.Series(m.addVars(sampled_tickers, \n",
    "                         lb = 0,\n",
    "                         ub = 0.2,\n",
    "                         vtype = gp.GRB.CONTINUOUS), \n",
    "               index=sampled_tickers)\n",
    "\n",
    "# [NEW] z_i: the i_th stock gets a binary z_i\n",
    "z = pd.Series(m.addVars(sampled_tickers,\n",
    "                        vtype = gp.GRB.BINARY),\n",
    "                index=sampled_tickers)\n",
    "\n",
    "# CONSTRAINTS\n",
    "\n",
    "# sum(w_i) = 1: portfolio budget constrain (long only)\n",
    "m.addConstr(w.sum() == 1, 'port_budget')\n",
    "\n",
    "# [NEW]  w_i <= z_i: restrictions of values of w_i so take it chose particular tickers\n",
    "for i_ticker in sampled_tickers:\n",
    "    m.addConstr(w[i_ticker] <= z[i_ticker], \n",
    "                f'dummy_restriction_{i_ticker}')\n",
    "\n",
    "# [NEW] sum(z_i) <= max_assets: number of assets constraint\n",
    "m.addConstr(z.sum() <= max_assets, 'max_assets_restriction')\n",
    "\n",
    "m.update()\n",
    "\n",
    "# eps_t = R_{i,t}*w - R_{M,t}\n",
    "my_error = r_it.dot(w) - r_mkt\n",
    "\n",
    "# set objective function\n",
    "m.setObjective(\n",
    "    gp.quicksum(my_error.pow(2)), \n",
    "    gp.GRB.MINIMIZE)     \n",
    "\n",
    "# Optimize model\n",
    "m.setParam('OutputFlag', 0)\n",
    "m.setParam('TimeLimit', 60*5) # in secs\n",
    "#m.setParam('MIPGap', 0.05) # in secs\n",
    "m.optimize()\n",
    "\n",
    "params = [i.X for i in m.getVars()]\n",
    "\n",
    "n_assets = len(sampled_tickers)\n",
    "w_hat = params[0:n_assets]\n",
    "z_hat = params[n_assets:]\n",
    "MIPGap = m.getAttr('MIPGap')\n",
    "status = m.getAttr(\"Status\")\n",
    "\n",
    "print(f\"Solution for w:\") \n",
    "\n",
    "for i, i_ticker in enumerate(sampled_tickers):\n",
    "    print(f\"{i_ticker}:\\t {w_hat[i]*100:.2f}%\")\n",
    "\n",
    "# check constraints\n",
    "print(f\"\\nchecking constraints:\")\n",
    "print(f\"sum(w) = {np.sum(w_hat)}\")\n",
    "print(f\"sum(z) = {np.sum(z_hat)}\")\n",
    "print(f\"w <= z = {w_hat <= z_hat}\")\n",
    "print(f\"MIPGap={MIPGap}\")\n",
    "print(f\"Status={status}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "97b2516a-14c9-436b-a6b9-9dfb973ea0b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['AAPL', 'ABBV', 'ABT', 'ACN', 'ADBE', 'AIG', 'AMD', 'AMGN', 'AMT',\n",
      "       'AMZN', 'AVGO', 'AXP', 'BA', 'BAC', 'BK', 'BKNG', 'BLK', 'BMY', 'C',\n",
      "       'CAT', 'CHTR', 'CL', 'CMCSA', 'COF', 'COP', 'COST', 'CRM', 'CSCO',\n",
      "       'CVS', 'CVX', 'DHR', 'DIS', 'DUK', 'EMR', 'EXC', 'F', 'FDX', 'GD', 'GE',\n",
      "       'GILD', 'GM', 'GOOG', 'GOOGL', 'GS', 'HD', 'HON', 'IBM', 'INTC', 'JNJ',\n",
      "       'JPM', 'KO', 'LIN', 'LLY', 'LMT', 'LOW', 'MA', 'MCD', 'MDLZ', 'MDT',\n",
      "       'MET', 'META', 'MMM', 'MO', 'MRK', 'MS', 'MSFT', 'NEE', 'NFLX', 'NKE',\n",
      "       'NVDA', 'ORCL', 'PEP', 'PFE', 'PG', 'PM', 'QCOM', 'RTX', 'SBUX', 'SCHW',\n",
      "       'SO', 'SPG', 'T', 'TGT', 'TMO', 'TMUS', 'TSLA', 'TXN', 'UNH', 'UNP',\n",
      "       'UPS', 'USB', 'V', 'VZ', 'WBA', 'WFC', 'WMT', 'XOM', '^SP100'],\n",
      "      dtype='object', name='ticker')\n",
      "['SO', 'ABT', 'AMD', 'MS', 'MA', 'INTC', 'SPG', 'GS', 'LLY', 'MDLZ', 'PG', 'TMO', 'USB', 'FDX', 'MO', 'AVGO', 'BLK', 'HON', 'GM', 'BAC']\n"
     ]
    },
    {
     "data": {
      "image/png": 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SFBREamqqtU58fLxDnfDwcOs1CW6EEOLMtzgxzeH8923HeWRM+yZd6kJybs4xLi4uzJw5k6NHj/Lqq68SHR3Niy++SOfOnav0/MTExODt7U1UVBSFhYX89NNPDr0sQgghzm0mk8bS3Sq4+XRybzzdXDiSVcyulPwmbZf03NTB082FxGfHNNl7N5bo6Giuv/56rr/+ep577jnatWvHxx9/zDPPPGOt888//+Dn50dYWBi+vr5OPT8iIqLKNPSKigqysrKIiIiw1klLc4z4LeeWOkIIIc5cm4/kkFFQhq+7K8OMa/jH8Aj3GG/m3wMd6RRV/WjA6SDBTR10Ol2DDw2daQIDA4mMjKSwsNChPD4+noCAgJN6Zv/+/cnJyWHjxo306tULgL///huTyUS/fv2sdZ544gnKy8txc3MDYPHixbRv316GpIQQ4iywZJf6g3RYhzDcFlxKiDGLbw0v8GxiJAxu1WTtkmGpc8z//d//cccdd7Bo0SIOHDjAzp07efTRR61TvesrNTWVLVu2sH//fgC2b9/Oli1byMrKAqBjx46MHTuWW265hf/++4/Vq1dz9913c9VVVxEVFQXANddcg8Fg4KabbmLnzp18//33vPPOOzzwwAMN/8GFEEI0OEu+zTWBu6A4y1r+2PH7qMjPaKpmSc/NuaZv376sWrWK22+/nePHj+Pj40Pnzp2ZP38+Q4cOrfdzKg9hWWZezZw5k6lTpwIwZ84c7r77bkaMGIFer+eyyy7j3Xfftd7j7+/PokWLuOuuu+jVqxchISFMnz7dYS0cIYQQZ6akjEL2pxcQrc+m/9q7rOXbQ8YR6OdHuFdQk7VNp2ma1mTv3gTy8vLw9/cnNze3yuygkpISkpKSiI+Px8PDo4laKM4l8m9OCHG2+uyfgzz/xy7eCf2Fi/K/V4XXz4fW54OmQQPPlqrt93dlMiwlhBBCCAdLEtP42zwLKqeojC9WJZFRUOpYZ1caHpQypmShKpg0RwU20OCBjbMkuBFCCCGEVUFpBXfO2cQtszeSnlfCE/N28Ozvidwye4O1jsmkse1oLpe5/INHeS4EtoT245qu0ZVIzo0QQgghrNLySigzmgBYvucEf2xXa6BtTs6x1jmWU0xxWTk3uf+pCvrdAfrGW77EWdJzI4QQQpxl6kqXTc4s4uU/d/Pr1uPWuoWlFXXeB5CRbxt++nt3Oh5uelwwEkyutXxvWj7D9FtppUsBd39IuPYkP0njkJ4bIYQQ4iwy/ZcdfPtfMhH+HnSK9OPFS7oS7OPuUOftJXv5efMxQAUr7cJ9uXHWem4ZEs/DY2re92ndwUwmfbLWev7PvhPodTo+c3ud8122woneENqOvWkFTHUxb03UazK4O7fQa2OTnhshhBDiLFFQWsF3/x2h3KhxJKuYv3am8cuW41XqJabkWY+f+yOR6z5fR5nRxAfLDtT6fPvABqCwzEhRmVEFNgCbvwIgOauIzvpDqqzL5Sf/gRqJBDdCCCHEWWLVvhOUGU3EBnlxbb9YAHbZBTIA24/msjtV7e3UKy4Q+5EoX/eaB2yyzTt7V+ZGhfW4zNVb1c3NJ0Rnft+AWKc/R2OT4EYIIYQ4S6w9qFYBHt4hjMFtQwDYlWoLbnKLy5nw/irr+Vc39aV7iwDrubGWnJsj2UUO5y2DvQCI1GVay4rK1P1avhryMrp4gueZt12O5NwIIYQQZ4n96QUAdIr0o2OkWshub1oBx3KK2ZuWz7HsYmvd2CAvvAyu/HR7f5Kzihj+xgqKyowUllbgXU0PTkFphcN5l2h/juUUE63ZtlEoKVCBjiFfDYWVe0fi0sRr2lRHem7EKfv8888ZPXp0UzfjjHPVVVfxxhtvNHUzhBDNiCW4aR3mQ4tALzzdXCirMDHu7ZXcMHM9T87fYa378qVdAXB10dMq1Acvg5qqfSK/tOqDgYISx+Dmit4taB3qQ4zuhLWsoiATTdPwKE5VBf7RDfbZGpIEN83E1KlT0el0Vb7Gjh3L8uXLq71m/7V8+XJmzZpV7bXPPvusxvctKSnhqaee4umnn7aWffrppwwePJjAwEACAwMZOXIk//33n8N9mqYxffp0IiMj8fT0ZOTIkezbt6/Rvj+natasWU7vkP7kk0/ywgsvkJubW3dlIYSoQ35JOal5JQC0CfNBr9fRIsgTgDxzYKLTgV4H8+4cwIA2IQ73h5hnVJ0oKOW53xMZ8uoyhzybwjL1jC7Rfvx0R3+GtA0h3M+DSGwbYmpF2eQVVxBq7s1xDYxppE97amRYqhkZO3YsM2fOdChzd3fH29ublJQUa9m9995LXl6eQ92goCAOHTqEn58fe/bscXiGv79/je/5448/4ufnx8CBA61ly5cv5+qrr2bAgAF4eHjwyiuvMHr0aHbu3El0tIryX331Vd59912+/PJL4uPjeeqppxgzZgyJiYnNZo+lLl260Lp1a77++mvuuuuuum8QQohaHDhRCECorzv+nm4AxAZ5szdN9ea4u+rZ8ORISspNhPq6V7k/1Ned5KwiUnJL+GZdMsXlRrYezWFY+zAACkqNAEQHeNIrTm16GexjwE9XaH2GriSHEwUl1t4c18AzL5kYpOemWXF3dyciIsLhKzAwEIPB4FDm6elZpa7BYABAp9NVeYanp2eN7/ndd98xYcIEh7I5c+Zw55130qNHDzp06MBnn32GyWRi6dKlgOq1efvtt3nyySe56KKL6NatG7Nnz+b48ePMnz+/xvdauHAhgwYNIiAggODgYC688EIOHLBNaywrK+Puu+8mMjISDw8P4uLieOmll6zvOWPGDGJjY3F3dycqKor//e9/1ntLS0t56KGHiI6Oxtvbm379+rF8+XJABWs33HADubm51t6sGTNmAPDhhx/Stm1bPDw8CA8P5/LLHadETpgwge+++672/3BCCFEPhzNVkBEf7G0tiw3ysh7HBXvh6+FWbWADEGruuVmx5wTF5SqQyS0ut14vNOfc2OfjhPi440WJ9dytNJv0/FJa6c3DUsFtTuUjNRrpuamLpkF5Ud31GoObV5NvPlaXVatWcf3119dap6ioiPLycoKC1F8CSUlJpKamMnLkSGsdf39/+vXrx5o1a7jqqquqfU5hYSEPPPAA3bp1o6CggOnTp3PJJZewZcsW9Ho97777Lr/++is//PADsbGxHDlyhCNHjgDw008/8dZbb/Hdd9/RuXNnUlNT2bp1q/XZd999N4mJiXz33XdERUUxb948xo4dy/bt2xkwYABvv/0206dPt/Zq+fj4sGHDBv73v//x1VdfMWDAALKysvjnn38c2ty3b19eeOEFSktLcXev/geOEELUx1FzsnALu4AmNsj2x2ecXdBTHUvQszgx1VqWV01w42MX3HSJ9gedLbhxr8gjt6icnjrzaEBwa2c/xmkhwU1dyovgxaimee/Hj4Oh9n+s9n7//Xd8fHwcH/H44zz++OP1fkZubq7DM3x8fEhNTa22bk5ODrm5uURF1f79efTRR4mKirIGM5bnhYeHO9QLDw+v8b0ALrvsMofzL774gtDQUBITE+nSpQvJycm0bduWQYMGodPpiIuLs9ZNTk4mIiKCkSNH4ubmRmxsLH379rVemzlzJsnJydbP8tBDD7Fw4UJmzpzJiy++iL+/v7VXy/6Z3t7eXHjhhfj6+hIXF0dCQoJDG6OioigrKyM1NdWhPUII4azkTPWHtn1vTesw28/rlsFeYDLC7ItAp4fr54PeNkBjCW7y7BKH7XtuCqrpubmwayRJy10gW517G/MoysskXJejCoIkuBGN7Pzzz+ejjz5yKLP0ltSXr68vmzZtsp7r9TWPXBYXq78iasuRefnll/nuu+9Yvnz5KefS7Nu3j+nTp7Nu3ToyMjIwmdTGbsnJyXTp0oWpU6cyatQo2rdvz9ixY7nwwguts7iuuOIK3n77bVq1asXYsWO54IILmDBhAq6urmzfvh2j0Ui7du0c3q+0tJTg4OAa2zNq1Cji4uKszxw7diyXXHIJXl62HzyWIb2ioibq/RPiXFOSB67u6quZSc4yBzfBtt6aAa1DmDauA9uO5DC5TwQcXQ+HzD3IBWngF2mtG+JT9XtS3bCUfc+NXq+jtR/W4MaNCoKOqhSDfJdAfD0DGuKjNTgJburi5qV6UJrqvZ3g7e1NmzanNv6p1+vr/Yzg4GB0Oh3Z2dnVXn/99dd5+eWXWbJkCd26dbOWW3o/0tLSiIy0/Y+XlpZGjx49any/CRMmEBcXx6effkpUVBQmk4kuXbpQVqay/Xv27ElSUhJ//vknS5Ys4corr2TkyJH8+OOPtGjRgj179rBkyRIWL17MnXfeyWuvvcaKFSsoKCjAxcWFjRs34uLiuKtt5Z4we5ZAcPny5SxatIjp06czY8YM1q9fb51ZlZWlZhmEhobW/I0UQjSM3KPw8SAIaQ83/dXUrWlwlkX27HtuXPQ6bhvaGuZOhQ/ng5fdH2QFqQ7BTXW5OI7BjcrD8TZU2t27rMDhNDL1bwDy3CM4s3aUspHgpi46nVNDQ+cSg8FAp06dSExMrLLOzauvvsoLL7zAX3/9Re/evR2uxcfHExERwdKlS63BTF5eHuvWreOOO+6o9r0yMzPZs2ePdZo5qHyfyvz8/Jg0aRKTJk3i8ssvZ+zYsWRlZREUFISnpycTJkxgwoQJ3HXXXXTo0IHt27eTkJCA0WgkPT3d+uzqPqvRaKxS7urqysiRIxk5ciRPP/00AQEB/P3331x66aUA7Nixg5iYGEJCQqrcK4RoYKvfheJsOLIWcpLPyG0BTlZmQak15yY+pNIfXQUnYOc8dVxkW3CPfMdh/rqCm+qGpQBrcJOoa00n7QDR2Wppj1KDcyMDp5MEN81IaWlplZwVV1fXRv3FOmbMGFatWsV9991nLXvllVeYPn0633zzDS1btrS2ycfHBx8fH3Q6Hffddx/PP/88bdu2tU4Fj4qK4uKLL672fQIDAwkODuaTTz4hMjKS5ORkHnvsMYc6b775JpGRkSQkJKDX65k7dy4REREEBAQwa9YsjEYj/fr1w8vLi6+//hpPT0/i4uIIDg7m2muvZfLkybzxxhskJCRw4sQJli5dSrdu3Rg/fjwtW7akoKCApUuX0r17d7y8vPj77785ePAgQ4YMITAwkAULFmAymWjfvr21Tf/8848scCjE6VBaAFvtZiYeXAE9a5/scDZZl6R6gTtE+BLkbbBdOLEHPlD5g7h5w8R34aeb1Hl+isMz7IObmEBPjmYX1zksBUCZmqWVGdIXThzAF9WDZPQIONWP1WhkKngzsnDhQiIjIx2+Bg0a1KjvedNNN7FgwQKHheo++ugjysrKuPzyyx3a8vrrr1vrPPLII9xzzz3ceuut9OnTh4KCAhYuXFhjXo5er+e7775j48aNdOnShfvvv5/XXnvNoY6vry+vvvoqvXv3pk+fPhw6dIgFCxag1+sJCAjg008/ZeDAgXTr1o0lS5bw22+/WXNqZs6cyeTJk3nwwQdp3749F198MevXryc2Vv3lN2DAAG6//XYmTZpEaGgor776KgEBAfz8888MHz6cjh078vHHH/Ptt9/SuXNnQC1wOH/+fG655ZYG/Z4LIaqx9VsotVsw88jamuueReZtPsrwN5Zzz7ebAejfulIe4JZvbMc+odD1cuh9ozrPT3OoGmwXFI3sqCZ05BTV0HOz9TvYNFvNGDYHN4Gdhjs8T/OsOSexqek0rZZdtJqhvLw8/P39yc3Nxc/Pz+FaSUkJSUlJxMfHN5uF5E6HK664gp49ezJt2rSmbsoZ5aOPPmLevHksWrSoxjryb06IU2SsgJzDMOdyyDoIkd0hZSu0HQ3Xzm3q1p0So0mj34tLyShQ2yWE+BiYdUNfNT27KAv++xSWv2i7Ibgt3LMBlr+iyntOUT05dia+v4pj2cW8c1UC132+jih/D/6dNgKAgS//zbGcYubd1JWEb7qDZoKuV8B29X0suzeRsnd64YMaHtvX5X7aXj6j8b8RZrX9/q5MhqXEKXvttdf47bffmroZZxw3Nzfee++9pm6GEM3bL3fCtu/Vsbs/DPifGpYpzKj9vrPA+kNZZBSU4u/pxp/3DibS3wOdTgdbv4ff76u6Btt48152vuYlKyoNSwHMvb0/RpNGWp4KmI7nljDt5+3cNKglx3KK0emglSFXBTZgDWwADF7+HPZsR9titUaYq8+Z23PTpMNSK1euZMKECURFRaHT6WpdndZizpw51pyHyMhIbrzxRjIzM+u8TzSeli1bcs899zR1M844N998s0P+jRCigZ3YawtsAEY/BwHm9aSKKgU3mQdg8xxY/Y762vGT6v04g/17QP1uG94hjKgATxXYaBoselIFNp6BqqK7Hzx6GFoNVecBLdRr9qEqz3R3dcHL4EqIj22I6tv/krnkg38B6Brtj3+FeaNMr0rBi5sXLjE9bae+Z+4s0CbtuSksLKR79+7ceOON1tkltVm9ejWTJ0/mrbfeYsKECRw7dozbb7+dW265hZ9//vk0tFgIIcQZY8dP6tU/Fq6YBTG91NAUQKHdH737lqhhKyplYcQOgBv/PB0tPSnHzLOj2tgt1EdOMhSmg94VHtilAjRjGdivNxNiXrMr6yAYy8HFrcqzfdxd8XDTU1Kuemjyzfk2g9uGQL55rbPI7mr22XGV74NeT1TH/rBP7UvoGxjWcB+2gTVpcDNu3DjGjRtX7/pr1qyhZcuW1j2B4uPjue2223jllVcaq4lCCCHOVEkr1euQB1VgA+Blnh1aXgjlxXBkHcxxXN2csM6QvhOS/4XX20G/22Dwg6ev3fWUkquCm6gAu3y8o+vVa0RXcPME/+iqN/pGqZlT5YWq9yakbZUqOp2OUF93jmQVO5QPaR0Ax4+ZnxMJ/i1swQ3gEWdb2sM/OIIz1Vk1W6p///4cOXKEBQsWoGkaaWlp/Pjjj1xwwQU13lNaWkpeXp7DV13OsRxr0YTk35oQTirOgU9HwEuxKjgBiB9iu+7uC3pzT0XuMfj9/qrPGPMC6MwL1RWkwdJnoSC9UZt9Mo7nqMAj0t9u82JLQBfTp+Yb9XoIMS/GemJPjdW8DY79G6GGUvrOGwx/P68KfCPhvDvVcZx55m1gvAp43LzAP6ben+V0O6uCm4EDBzJnzhwmTZpk3ena39+fDz74oMZ7XnrpJfz9/a1fLVq0qLGum5v6H0KWyheni+XfmuXfnhCiDrv/gGMbbNO+YweoX7gWOh14m3tv/npcDc34RMAd/9rqxPRRQy72En9Rr2VFKoBqYpkFpRwy7yUVHWAObirKbO3sML72B4SY8/0y9tZYxVTpj6tLo3LRFdoFeb4RENYB7tsO1/6gyvR6uHmJ+n561D5jqSmdVbOlEhMTuffee5k+fTpjxowhJSWFhx9+mNtvv53PP/+82numTZvGAw88YD3Py8urMcBxcXEhICCA9HT1H9fLy0slcAnRwDRNo6ioiPT0dAICAqps+yCEqMHBZeq13x0w6D7wDlMBjT2vEDVTaJ95C4ZxL0N4Z7jqWzWU4+6jpkgvmaGSZrd9D4ufVsM3K1+H41vgno3g67i57+miaRqj31ppPQ/3Mw9LHVkLJTngEw4tq19N3cqSd5OxT71mHoD0XRA/GDz8ATBV6jjuF2YE+3VgQ80BUuWVnn3P3OEoi7MquHnppZcYOHAgDz/8MADdunXD29ubwYMH8/zzzzvsU2Th7u6Ou3v9N1Cz7HtkCXCEaEwBAQEOO40LIepwcIV67Tih5l+yoe0hbbs6bjcOOl2sjjvYpTBEdIXrflI9NQXpKmj6+nIwmRe1O7wKulTK1TlN9qTlk1mo9swL83XH4GoeZEnfrV6je4O+jj+ILHk2GXvVZqIf9gdjKXS/Bi5RGyxX7rnpElBqe/6oZyBuYIN8nqZwVgU3RUVFuLo6NtnyF29D5S7odDoiIyMJCwujvLy87huEOElubm7SYyNEdUpy1ZTnyjtOF2aqmUIAUQk13z/hbQiKB50eBj1QtWfHnsELrvkefroZdv1qK2/CHJxFO20rC394rW3qNSfMwU1ou7ofYu252QvpiSqwAVvQB9wzvA33f6/WrHnuos6ElZqvRXSBlo27un1ja9LgpqCggP3791vPk5KS2LJlC0FBQcTGxjJt2jSOHTvG7NmzAbUr9C233MJHH31kHZa677776Nu3L1FRUQ3aNhcXF/nFI4QQp5OxAtZ9DMteVENH920HUwW4GNSU57c6qXr+sSooqYm7Lwx/sv7v6+oOl8+EPx6ATV+qsuxDUJqv1sRpPw6ie530x6oP476l5Cx8gYoB9/PFajXa8PoV3end0m5zSkv+TGiHuh8Y3FoFd6V5sPkrW3necevhxT2i6RzlT8tgb9U79Ls5oPM+c6d411eTBjcbNmzg/PPPt55bcmOmTJnCrFmzSElJITk52Xp96tSp5Ofn8/777/Pggw8SEBDA8OHDZSq4EEKc7Yzl8OVE2wyo8kL47lo1XBTVEyK7qUAHbDOBTpKmaczdcJROUX4E+xhYuisdH3dXYrrNoKNvPN4rZqjgZtGTsHEWJK+Fqb+f0nvW6sQeXOZcSjCwbP5r5JQ/yoDQEi6OyATsZiRZe27qsTioqzu0Hg77l8Dmr23lRZlqirybWhSwXbiv7ZqlV8xHgptTMmzYsFqHk2bNmlWl7J577pHVcIUQork5ukEFNm5eKpcm6yDsX2y+9h8c32SrW1pwSm/169bjPPLTtmqvne9aykxXYO9CW2E1K/02pPKlL2CZL9lSlwpofJN/I3wK3J+o1rIpzIRC88rBIfUYlgLVG/XDZFsStkXecdWzU1mB+fneZ+7Kw/V1Vk0FF0II0Uyl7VCvLQdDj2uqXrf02gD0v+uk30bTND5afsChLD7Em/NaBRHp78HOimrWbsk7robMGkNFKeyxBVIRumzGh9jt5m0Zisowr1fjHwsG7/o928MPrvkBet8EoR2ts6TIPVp9/QLz+zaD4OasSigWQgjRTKVaklm7Oi5Q5x8LLfrYtlq47HPodNFJv82q/RnsTs0H1D5KXWP8mTauA74ebpQbTQx7bTkrC7syxMXcHr2bmkGVn2Lbs6khHduEm1ZKruaFr64YT10ZrwX9CpbOqXLzCsLODEnZczXAhW+q49kXwcHlDnk3VmWFand1qL5X5ywjPTdCCCFOL01TX/bnRzeo44guKsfGwuANF7yuhmJi+qjp2aew/tgnK9XeU1MHtOS3ewbx4iVd8fVQg0JuLnoSYgOYXjGVw0GDYMrvtu0NPj1f5QU1pOzDVCxXOaOrTF3AT/UaeSUvt9VZ8z58cB4seUadOxvc2DM/n7xqem5St6udwH0izop1bOoiwY0QQojTR9Ng/p3weltbD8LOeWqvJ1cPteKw/cq3RZngFQR3roWbFp9SYJN4PI9/9mXgotdx06D4aut0ivLjkBbJ66HPqwXvLHkohSfgyH8n/d5VbJsL7/fGNWkZJk3HX24j0Yd3VNcCYm07ch9eDSd2qcX7oO7F+2pjCdRyj1W9dnyLeo3qcfLPP4NIcCOEEOL02fsXbP1GBQt7zSsI71ukXvvdZlsVuL15wb3zblevepdTCmw0TWPZHjUbaGi7UFoEVT+VvFOkCqx2Hjdv79D7BtvFlC0n/f5VbP0WjGVkBvfkwrIXSA0bAuNehQnvwB1rHBcQ1LvB1AVwzyZoP/bk39PPHNzkHVPr+CSvs12zfLbIHif//DOI5NwIIYQ4fbZ9bztO26leLbOR7Pd7uuwztRpx29Gn/JaHMgq559vNbD+mApa24T411rVMjU7OLKLCaMJ12DRMqTvQJy2HlK2n3Bar4iwA/g66hsRjUUwK8VYLDwaZe5Q8A211o3tBywZYLdgS3KTvgo8HQ0Eq3PWfGuqSnhshhBDiJOSlwM6fbeep5unYWUnqNbCl7ZrBW22X4HJqf4On5ZUw8f1V1sAGILaGXhtQ+zgZXPRUmDRSckvA3YeXsoYBUL5/+SlPQ7cqUsHND7sKAejXKsjxun1wE9uvYd7TOix1RAU2ADnJKpnYMhurmfTcSHAjhBCi8VWUwYeVfkmn7lDBguUXbWD1eTCn4of1R8grcZzGXVtw46LXEROkduHeejSH535P5Ku0OI5qIbgVpakEXwCTERY9pRb6K8xwul0mc3CTYfRhXJcILkmIdqzgEWA7jujm9POr5Rddtays0DGZ2K/qHo1nIwluhBBCNL6d89SeUQDDnwJXT7UKsWWBOXd/x94KJxWUVvB/Kw4w7LVlPGZeoE/TNH7cpGYGjetimwFUW3Bjf/3+77fw+aokSnDn3YpL1DMPmNubtBL+fRf+fQ++maTWq6kvYzn6MjUd3T8ojFcv74aucj6RZU0agPAu9X92bTz8IKi1yuGxzJwqL2p2Q1IgwY0QQojGpmmwTu1EzfAnYchDEN5Zne/+Q70GxJ50wvDhzEKGvbaMl/7czaHMIn7YcITiMiPrD2VzOLMIb4MLD42xTaGOCvCs9XmW4KbcaJuuvs5knsl0fBOUl6ikXItjG2DhtPo3uDgbAJOm45bRPa1T0R1oJttx8KltN+Hgxr/gng0QbZ5uX1bY7JKJQYIbIYQQje3If3B8M7i4Qy/z7KNI81DLweXq1Tv4pB//0oLdZBSUERvkha+HKyYNdqXmMXfDEQDGd4ukdagPn03uzZyb++HmUvuvvh4tAhxeAQ5r4ZzQ/NAZy1QwYF7Nt8AtCA0dbPgcdvxc9WHVMQ9J5eFFXIhv9XVaD1fr/fS/+5Tzjhz4hKrcJssqx2WF0nMjhBBCOM3Sa9PtCvAOUceWPJL8FPXqdXLBTUFpBX8lqpydTyf3pmesGtracCiLP7arZ1/eS60sPLJTOAPbhNT5zIt7RLPgf4OZe3t/u1IdG0zm3p/kNWj5KriZXTyIb/QXqvLE+fVqc2m+ytHJ1nxq7kUyeMGty2DMC/V6ptPczENzBWnNLpkYJLgRQgjRkE7ssW2lAKpnIPFXddzvdlt5ZKUkWU/bbKFlu9NZtju9Xm+3KyUPTYMIPw/aR/jSOUqtU/PJyiSKyozEBXvRp6VzuTx6vY5OUX64uei52W6xP1tws47cE2pY6oTmz38lKngqLciq1/NzM1UwlqfzJdCrmiGp08FgDm72L1VDYIHxzSaZGCS4EUII0VAqSuHzUfDxIPjrCXWeexQ0o0oYjuhqqxvWGXQutnMvFdx8svIAN8xazy2zN5BbVPd2BzvNU7wtQU1csPqlnVGgEny7xwRUTdZ1wrQLOrL8oWHcODCeDSbzbtxH1mK0rK7sE4G7rwqeMk5UE5DlHYf/Gwrv9bYGfblZql6Jq/8pte2UGMxr/Vh6bVoOapp2NBIJboQQQjSM1B22GVFr3ofnw+CDvuq8cq+Am4fjPklewXywbD8vLlAbRFaYNPak5Vd5C5NJ498DGfyXlEV+STlLzT08luAmzM/DoX50YO3Jw3Vx0etoGeJN6zBvdmotKdW5Q3E2wZmbAAgIi2FUgvocruVV28ueP1WOTuY+WPd/ABTnqJ6bMvegqvVPF7dKM8ZaNNBaOmcIWaFYCCFEwzi2Ub0Gt1Ur8BZl2q5VtxljRDdITwRU/snri1Qvgr+nG7nF5Ww7mkOHSF/87GYTPTh3K/M2qyEhnU5NxNLp4PwOYQCE+1YKbuqYGVVfHSP9qMCVbbSlDzus5W7+Ebh6q0DBy1hNcJOTbDveswBMRnQFKiAr96w7/6fRGCoFNyeZ83Smkp4bIYQQDeOYeWfvrper/ZHsF+Xzjapa3y7v5niZF5oGrUK9uaqvymF5/o9dDHzpbzLNQ0zFZUbmb1GBjcFVj6apQGjm1D4kmBOJw/3cHd7iVHtuLDpE+KLTwb/lba1l2ZoPbiEtMfio9/bSCh13OwfH4KYoE5LX4FqsEorLPZowuHHzdjy336y0GZCeGyGEEKfOZALLAnct+qkNMG/8C94w56l4VTMEY7fybmq5F1BBlL8nHSJs06PzSyvYdjSX2GAvRryxAoBALzc2PTWKI1nFhPq642mw5e4Eehlw1euoMKkgI6aBem68DK7EB3uzMLMv97rOA2CVqQuh/r64m9NXXDBBWQG4203vzjkMgObuh640D3b9hqFU9WiZvEMbpG0npXLPjXvzCm6k50YIIQSkJcLK15xbadfesY1QmK5+ScaZN3n0CbNdLyuseo9dgvGxMvXLNtzPg3FdIh3WmEnOKuKj5Qes561DfdDpdMQGezkENqBmOlkCG2i4nhuAjlF+7NLiWNP6XjJ1gXxcMZEwX3c8vbwp08ztMC/QZ1GeeQiAH9wuUgW7fsOrTAU3Ovvvz+nWzHtuJLgRQohzXVEWfHUJ/P282itp/l2Qsc+5Z2z/Qb22HQWuBnWs09l6BFoNBaCswsSC7SkUlxnBMwAGPwQJ17OvJACASH8PPNxc+PH2/ozqFA7A4cwih9EeU+Whn0rcXdWvtu4x/ngZGm6AolOk+izfuV3C+dr/sVNrSaivO74eBgoxB1FvdwWjeZZXWSFuJSqQeSPjPMr1npB3jMgy1Zvj5h/eYG1zWjPvuZFhKSGEOJdpGvx2r23zyv8+Ua9bvoaE69Qque3GgH+M432Jv6hACMA3Eo6sU8cJ1znWu+Nf1avT6SKW7Unn9q82Ulph4pbB8TwxvhOMeAqAlC9Vvk6Ev0oIdnXRM7RdKIsT0zicWUhxudH6yN4ta59l9ME1PZm/5RgzJnZ28ptRO0tw819SlnUzzsgAT0rLjQTq7HYL/2YSXPS+dYHCE5of6QSyXOvBKNZYqxn8q0myPl0qz5ZqZsGN9NwIIcS5bOt3sOvX6q9t/hr+eAA+HgxGx5212fSVSpbNSbYFNtG9IX6YY72AFtD5YuZvOc4NM9dTWqH2TPr0nyQ0ux6Y1LxiQPXcWFjWrPkvKYvtR9UU88FtQ7h/ZLtaP9LITuG8f01PQnzca63nrI7m4CYltwSAYG8DPu6ueLtX6ic4sBR+vBHSdgKw2xQLwK+lvaxVyjUXvPybcFjKss4NqCGqhtzi4QwgwY0QQpzLVr6qXoc87Fh+xSw1ZKRzUdO6zXspWRWpGT8Muh8u/wKu+BKm/Ab6qr9Wlu1J56G5W6uU70tXvR2FpRXsTVXHLUNsuSDdWwQQHeBJfmkF+aUquHrnqoQqeTanS7ifO0HeBut5C/MGm+6uelabKu3cnbzGFtxosVzYLZJlph7Wy+tMHfD3dpy2flr52g2J2W/S2UxIcCOEEOeqgnTIOgjoYMD/4OKP1HHHCdD5EjVkZFmfJj/V8d5C8xo27cdDl8ug88VV8ziAknIj9367mQqTxkU9ovjpjgHWHpnE43kArN6fQZnRRIsgT1rZBTd+Hm4sfXAor1zWle4tAri8V0zTbVcA6HQ6OkbaZkJZghudTsdTLvdSojm2reLYFkD13Ey7oCM6dz8+rxjHUS2EJypuIqAJP4vDjK6K4qZrRyOR4EYIIZpCYcbJ3bd7AXw+Ru3hVB+aBrnH1Cyef9+HrCTbtf1L1GtYRzVbpsc1cN92uPQzWx1LcFNQKbix9NzUspv3oYxCRr65grySCrwNLrx2eXd6xQVyXry6JylDzaD694AKlM5vH1ZlOwIPNxcm9Ynll7sG8voV3ZtuuwIzS94NQGyQbSZWqXsIPxmHONR1ObYegGLvaKIDPLmqbwueq7ieQaXvcliLwN+zCYObZk6CGyGEON02fw2vtYY1Hzh/7483wJG1anZTfSyZAW91glkTYNET8G4POLgc8lLg9/tVnRZ9bfUDWqitESx8LD03KbaysiIoL1LHtaxsO3XmfxzNVr0CMYFeGMyzmCxDT4cyVXBz4IQakuoS5V+/z9SE+sbbPq99e93dqv461WkqCTokIg6AUZ1sCcTRAZ64uTTxr2D7vJtmpnllEAkhxNngl7vU61+PQ6+pYPCutbpVWSFUqGRW8o7B8S0Q1aPm+rlHYe2H6jjNbqfuOVdA6xHqWa6eMOSRmp9hyc3It8u5yT2iXvVuVWbZ5BSVkXg8j/zSCg5lFlnLowJsAVN8iBrOsVw/eEIFOa1C6/l9aEIjO4bxw239KaswMaC1LdA5eKIQV1djtffExKmVmhNiA6xlQ9o14QJ+Fj0nq38fYQ07q+xMID03QghxOmUecDzf/HX977XMSrJY+Vrt9Ve9BcYyxzLPIFW29091Puwx8I+u+Rm+5g0v9/ypViEuybNthukZqNayMTOaNC545x+u+Wwdt3210eExEf62IZy4YBXEJJ0ooLjMyLEc1bvTKvTM70nQ6XT0jQ9iUNsQ9HrbZ78kIZq/TL2r1C/U3Gkbo3ps3Fz03DeyLZ0i/bh/ZNsqdU+7EdNh/Btw7Q9N3ZIGJ8GNEEKcTpWDm3/fty36Vt97Q9oBOtj9u1pZuDq5R2HT7Krld66FXjfYzlsNq/09fcw9N2nbYdcvsHeh7VphukPV9PwSjueWoNepXoqBbaofsmoV6o3BRU9eSQUr9p4A1B5RTZksfKqmX9iJv00JXF32BOeXvmEtT9cCCLfbqfy+ke1YcO/gKruXNwk3T+hzc9U1jJoBCW6EEOJ0siTixg4A71DITYad8+p3ryUJueUg6DRRHW+ZU33dVW+rHpq4QWqXblCBim84XPiW+ot9xHSI7F77e7YdbTtO3wW7fquxqiW/JjrQk3l3DmTOzedZr4X52taccXd1oUu0Gs76davaCDMu2KvJk4VPRaC3gTGdI1hj6kySFsk+k+oNSyewymaeovFJcCOEEKeTJUDxj4E+t6jjHT/X817Vy4F3KLQZqY7TE2HbXPi/oXDYvPpteTFs+14dD3kIblgAgx6AC99WZTqd+ot98IMOw0rV8otU94KacZW00nbNflYVcMwS3NhtVvnBNT0Z2zmCmwbHO9TtFad20l6+R32m0AZecK8puNqt8bPZ1AaADAII9DLUdItoJJJQLIQQp5M1QAmBaPOKteado+t/byiEdlDHJ/bA5tmQskXNoLpqDpTmQ2ke+LeA+KFqYb2RT598mz1VIMKR/6AkRyUhTzsCLo7DSJbcmegA23o347tFMr5bZJVH9owNBJIoKjPPKGoGwY2LXQ7Or6YBjNfWstnQm/H6s7dH6mwlPTdCCHE6FZkXv/MKVtOuQfW+zBxfd+6NfWAUYt6CIO+Ybe2aimL49irYOFOdtxlZ7YrBTvMMUK8pW9RrTO8qgQ04DkvVpae558Yi2Ofs791oGWwL6vZ496ZL6edsCLygCVt07pLgRgghGlNFGexdpIZ0wDYs5R3qmMh5eJV1uf4a2ffceAbYZjJZpmYHt1F5NgeXq3NL8HSqPAIcz1v0q7baYfO6NTH1CG7C/Twc6gU3g56bW4e25sJukXx8XS96xwWioZd8myYiwY0QQjSmTV/CN1fAe71hyzeOvS+V17epa3jKeq95w0WfcMfrnSst7OdXyxRvZ3g69rIQe16VKkaTxjbz5pb1XYxPDU0pIc2g58bH3ZX3r+nJ2C4RjOio/tt0ijzzFyZsjiTnRgghGlOKecPIogyYf4et3Cukat2sg47nmmZL+M1JhhIVPOBtvtd+fyCAmL6O535RJ9fmyizDUgDoIKZPlSp70/IpKFXbLLSP8K1yvTq94gL5detxoHnk3Ni7rGc0PVr4Ex9y5q/d0xxJz40QQjQmSz5Mm1GO5YFqSX66TbKVLZlhC2DSEuHlWFj0JKz9CN7uqsp9wm3DRPY9P+5+ENbB8T18Gyi4sR+WCutYKdiB3OJy3v97PwA9YgMcEmtr08su76Y55NzY0+l0tAnzrff3QjQs6bkRQojGlGVeeG/YNGg/Dv59FwY/ZNuQ8sK31arB6z5S57Mvgsm/wK/3qBlP/74HLna/+Ec9Z0sStt8byCu46jCUX9VZSifFfliqUr5NQWkFl364mgPmLRSmDnCc8l2bDhG+BHsbyC+tICqg7jwdIepLghshhGgsZYW2DSeDW0FML+hzk2MdgxcMvFetS1OcBcc3qx4be8YyiEqAC15XM5Ws99r13HiHgN4FQjvCiV1q2Ku+e1bVxeANelcwVUBsf2uxpmlM+3k7B04UEuDlxkuXdGVUp/BaHuTI1UXPj3cMoKisAj+Ps3d1YnHmkeBGCCEai2VIyiOgalKuPb9IeDRJ5efMmgCluY7XPfzhqm+r9sTY59xYcnim/qHWvQnrdMrNt9Lp1NTzzAMQP9haPGddMr9tPY6rXsfnU3rTKy7I6UfHh5z5m2WKs48EN0II0Vhyj6pXS35NXSK7w/Xz4O/noPtVqsfmz8dgwrvVDzHZD0tZkoy9g2HQ/afW7upM/lUt4GdOUt6fns+zv6t9rR4Z2/6kAhshGosEN0II0Vgs68/4ObExYUwvmDzfdt7jupoX4qs8LNWYfELVl9nbS/ZRVmFiSLtQbhncqnHfWwgnyWwpIYRoLHlqU8hT2nW5thWG3e0Tihs5uKlk42G1KOEdQ1uf1RteiuZJem6EEKKxWIalTiW4qU11w1KNTNM07v5mMym5JQB0i5FF6sSZR3puhBCiMWyYCdvnqmP/BlopuDJD4/bcrDuYyasLd1NSbrSWHThRyB/b1QywYG8D3u7yN7I488i/SiGEaGiJv8Dv99nO/Rtoj6fKHHJughv00RsPZzH5i/8orTAR6uvODQPV+jW7U/Osde4Y1rpB31OIhiI9N0IIUZmxXG19cLL3LnnGdt79Gojq2TDtqsxhWCq05npOOnCigJu+3EBphQmAnzYdtV7bk5oPwFV9WnCzJBKLM5QEN0IIYS/zALzSEn6YDCZjndWr2DJHrUrsFQzTjsIlH4FLI3WSaybbcQMNS+UWl3PjrPXkFJXTJdoPF72OHcfyuOyjf0nPK2G3Obip7/5RQjQFCW6EEMLezp+hrAB2/QqLp9dcL2MfvNEB3uwEX06A3X9AeTEsf0VdH/xQ1Y0tG1qw3bCQm0eDPPLD5fs5nFlEdIAns27oS5dolTC88XA2by3Zy4ZDWQDWciHORJJzI4QQ9g6usB2veR9CO0DP66upt9y2tULeMdXj0+92yD+u1rXpfWPjt9UrCO7bAW5eDfbI9UkqeHlwdDtCfNxpHerN1iM5ACzbfYLsonL8Pd1IaBHQYO8pREOTnhshhLAwVsCRdeq4+9XqddETUFFWtW5JjnptM1Ltu5R3DBY/pcrOn9ZgPSmV/bzpKINf/ZvE4+bE3oAWp5xMXFRWwcIdKXyy8gCbknMA6GEOXm4caNsIMzVPTf8e3iEMVxf59SHOXPKvUwghLPKOqi0PXNxh4vvgHQYlufDfJ1Xrlpj3fwrrqDa1tAhqBd2uarQmPvDDVo5kFXPf95sb5HkfrzhAj2cXc/vXm3hxwW4AvAwutAxWM7G6RPvz1qTuDveM7Fj/zTGFaAoS3AghhEVOsnoNaKGSgDtcoM4XPQFH/nOsawluPPyhzShbea+pDZJAnJ5fwgXv/MPM1UnWsgqjLYF4b1oB2snO6LIz+99DlFWYaBHkSe84tbnnuC6R6PW2VYfjQ2yzstxcdAxpd3pXQxbCWZJzI4QQFtbgJla9nncXbJyljo+sgxZ9bXWtwU0AJFyven3SdqrjOiRnFvHe3/u4ZUgr2oVXn3T8wd/7SUzJ45nfEq1rzOxLL3Cocyiz6JR21TaaNNLySwGYe9sAIvw9yC4sq7IwX7ifu/X4vFbB+Hq4nfR7CnE6SHAjhBAW2YfVa4B5F+/QdjDkEVj5qpodZc++58bNAya+V++3eWXhbv7YnsJPm46y45kxeBmq/ijOKS63Hh/PKWbj4WwyC0od6qzYk058SHzlWx2k5pbwx/YUa4AS7udBz9hAXPQ6krOKMJo0XPU6Qn3V9UBvQ5VnhPi4o9OppX9kSEqcDSS4EUIIi8o9NwAh7dRrxj617s3mryG8i2Nw44TSCqN1+wKTBi8t2M1zF3exXs8rKWfZ7nS2H8u1lo14YwXF5VXX3Fm5L4OpA2sPbp77PdH6fhZxwV4MbBPCN+vU5w3388BFX/Pml24uerrFBHDwRAFjOkfU/SGFaGIS3AghhEWOuecmMM5WFtJWvWbuU+verHkfgtuCZg42nAxuth/NdTj/au1hvN1d6RDhy+HMIj5fdZC8kgqHOpUDmzGdw/lrZxprDmRSWmHE3dWl2vcqKTdWCWwADmcWcTgz2Xru61H3r4Kvb+pLcZmRML/GmQUmREOS4EYIISysPTeVghu9KxSeUIENQHaSbV8nJ4Mby27afeOD6BDhy+w1h/l4xQGHOoFebmQXqWGp6ABP+rUK4udNx6zXR3QIZ+PhHDIKStl4KJsBbapP8F1zMNN6/OaV3bkkIZriciPfrz/CZ/8kcSynGICkjMI62+3r4Sa5NuKsIbOlhBACoKIU8o6rY/vgxuANHSc61jVVnNSwVLnRxFpzwBHm686E7lFV6tw8KJ4NT47ih9v68/OdA1j92HAeGNXOoU50oCdD2qqAZvWBjBrfb0liGgDX9ovl0p4x6HQ6vAyu3DAwntWPDbfmz1zTL7bGZwhxNpLgRgjR/BWcgM1zoKyo5jq5RwFNrfbrXaknZMjD4BkEfW4B/0qBgBPBzasLdzPHLs+lXVjVmVJ94oNw0evoGx9Ez1g1NTvS39MhJyYqwJNuMep996UV8Me2FC7/6F/Wm7dGADCZNJbsUsHNqE7VJwG/f00Cb0/qwUOj29f7MwhxNjjl4MZoNLJlyxays7Mboj1CCNHw5lwOv9wJS56uuY59MrGuUnJteCd45CCMf12tgWPh6unU1gef/mNbsybM1x1/r6rDPFH+nlXKXPQ6Ar1ss5gi/T1oFarWnjmYUcg3/x1mw+Fsrv10HfM3H6OgtIJ7vttMWl4p3gYX+reufgVjDzcXLk6IrjL1W4izndPBzX333cfnn38OqMBm6NCh9OzZkxYtWrB8+fKGbp8QQpy6lC3qdfvcmutYkokDahiisQQ8/jG2sshuVQOhSgpKK/h05UFm2S3GBxBmt3aMvaiA6hN2L+8Vg6+HKy9f2hUPNxdahaqcn8OZhZwwr1VTZjRx3/dbuOPrjfyxTSUSD2wTUmPCsRDNldPBzY8//kj37mop7t9++42kpCR2797N/fffzxNPPNHgDRRCiNMi05zUG9Sq9nqBLW3H9tsu1GDO2sO8sGAXM35LdCgP9VFBzPy7BjqUB1WzzgzAY+M6sO3p0VzVVwVfUf6eeLjpKTdq7E1Ti/t1jvID4J99tjyc8d0i62yjEM2N08FNRkYGERFqnYMFCxZwxRVX0K5dO2688Ua2b9/u1LNWrlzJhAkTiIqKQqfTMX/+/DrvKS0t5YknniAuLg53d3datmzJF1984ezHEEKcizRNbY45czzMuVKdW1iCm+A2tT/DsqEmQFinOt9yT1o+oNaWsfS2gC2I6dEigIdG2xKGdbX0BNlf0+t1xAY5DomN7uS4Bk33FgFM6FY1aVmI5s7pgdbw8HASExOJjIxk4cKFfPTRRwAUFRXh4uJc12dhYSHdu3fnxhtv5NJLL63XPVdeeSVpaWl8/vnntGnThpSUFEwmU903CiEEGpzYDYdXqdPjmyG6pzrO3K9eg1vX/oigeBj/Juz9CzpfUuc7HjJPs354THsu7BbFH9tSSM4qopO5lwVg8oCW/LMvw+leFn9Px5ydhNgAh/M7hrZy2CNKiHOF08HNDTfcwJVXXklkZCQ6nY6RI0cCsG7dOjp06ODUs8aNG8e4cePqXX/hwoWsWLGCgwcPEhQUBEDLli2dek8hxDkuY6/tePfvKrgxGSHroCqrq+cGoM9N6qseDmeqGVqWXbarC2D8PNz4/rb+9XqePft1ZzzdXOgQ6Tj7KlwW3BPnKKeDmxkzZtClSxeOHDnCFVdcgbu7SopzcXHhsccea/AG2vv111/p3bs3r776Kl999RXe3t5MnDiR5557Dk/PqjMMQA1jlZba9mPJy8tr1DYKIc5w9sHNodXqdf1nYCoHN2/wi6n+vpOQV1JOZmEZoIalGpqP3SynIG8DoT7ueLjpKSlXvdkR/hLciHPTSc3/u/zyy6uUTZky5ZQbU5eDBw+yatUqPDw8mDdvHhkZGdx5551kZmYyc+bMau956aWXeOaZZxq9bUKIM1SF3WaTGnBij+08fRfsXgALzX+YDX0E9A23/Nc+c6JviI97o6zua79tQrCPAZ1OR9swX+u+VKE+1c/IEqK5O6ngZunSpSxdupT09PQq+S6NmdxrMpnQ6XTMmTMHf3+1gNWbb77J5Zdfzocfflht7820adN44IEHrOd5eXm0aNGiSj0hxFns8Bo4sBTSdqrcmZ5TYMDd6lppvq2eZnLsuSnNhe/MCcI9p8DAexu0WZZF9SrnwjQU+4DJsg7OS5d25eIPVtMpyg9XF1mnVZybnA5unnnmGZ599ll69+5tzbs5XSIjI4mOjrYGNgAdO3ZE0zSOHj1K27Ztq9zj7u5uHToTQjRDhRnw5YVqSwSLFa9C31vB1WDbJgGgLN8W3Lh5Qbl5xeLAeBj/Rp1r1jhrfZIKbvrFBzXocy3se25CfdXPuS7R/qx85HxZmE+c05z+1//xxx8za9Ysrr/++sZoT60GDhzI3LlzKSgowMdHrc65d+9e9Ho9MTENN04uhDiLZOxTgY1nIAybBitfh8J0OLQS2oyE0kp5dsYycHGHNiNg12+qzD8GXBp22KjcaOI/c3DTt5GCGz+74CbCLnk4KqD6HEQhzhVO91mWlZUxYMCABnnzgoICtmzZwpYtWwBISkpiy5YtJCerZdCnTZvG5MmTrfWvueYagoODueGGG0hMTGTlypU8/PDD3HjjjTUmFAshmjnLtgnhXaDfbdBhvDrf+r16tR+WsghpC152+0e5V93j6VQczS6i7RN/kl9aQZC3gS5Rzu0cXl/2w1KSPCyEjdPBzc0338w333zTIG++YcMGEhISSEhQq3w+8MADJCQkMH36dABSUlKsgQ6Aj48PixcvJicnh969e3PttdcyYcIE3n333QZpjxDiLJRr2RPKvJN3T3OvcuJ8NWRlPyxlEdLOccNLd7+qdU7BTxuPWY8HtglptLVmfGvouRHiXOf0sFRJSQmffPIJS5YsoVu3bri5OXblvvnmm/V+1rBhw9DsVwitZNasWVXKOnTowOLFi+v9HkKIZs5+w0uA6F4Q1ROOb4LNXwHVBBahHRyHoRq45+bv3WnW49uG1LGdwymwnwouPTdC2Dgd3Gzbto0ePXoAsGPHDodrpzO5WAghgKrBDUCfm9Uu4P++r3ppKgttB0VZtnOPhuu5Sc8rYetR1Vv03+MjCGvEHhVPg21VeAluhLBxKrgxGo0888wzdO3alcDAwMZqkxBC1K28WE3tzjSvLBwYZ7vW5TJY+xGkbYdk8yaSlt4cgJD2kG63kWUD9tws25MOQPcY/0YNbECtbGwR5FX9hptCnIucyrlxcXFh9OjR5OTkNFJzhBCiHozl8NlIeCXelnMT1tF23c0DJv8CEV1tZbHn2Y6DW4NHgO28AYObJbtUcDO8Q3iDPbMmLUO8ee7iLnx8XS/ZQ0oIO04nFHfp0oWDBw82RluEEOeC9F2q18Xejp/h89GQe6z6eyrb+i2k7QCjefVhv2g1FdyedzBM/hXajIKOE6H3jarcvwW4uldKKG6Y2Uwl5UZW7VM9RSM6hjXIM+ty/XlxjO0SUXdFIc4hTufcPP/88zz00EM899xz9OrVC29vb4frfn4NO+tACNGMHF4DM8dC/BCYYl5jpjQffrxBHa//DEY+XfszjOWw8jXHsrBO1df1CoLrfrSd37EGfMxBh0Nw0zA9N2sOZlJcbiTCz4POUfKzUIim4nRwc8EFFwAwceJEhwRiTdPQ6XQYjcaGa50QonnZs0C9Jq1UicABsbBznu26ZcXg2mz9zpZEbBHds37vH24XBDkENz71u78Of1uGpDqGyQQLIZqQ08HNsmXLGqMdQohzgf0v/F2/Qf+7VG+ORd7x2u83GeGf19Vx96tVoOMbCefd6Xxb7IOb6qaLO0nTNJbuUlPAR56mISkhRPWcDm6GDh3aGO0QQpwL8lNtx+m71OuRtbay3KO133/gb8g+pPJrxr+hViT2jwXPAOfb4mY3k8nn1IOR5KwijueWYHDVM6B1SN03CCEajdPBzcqVK2u9PmTIkJNujBCimbMPbk7sgX/ehCy7CQrZh2DZi2r/p+FPgd7F8f6t36rXbpPA4A1RCafWnqu/g4I0tR3DKTqWo5KkYwI98XBzqaO2EKIxOR3cDBs2rEqZ/diy5NwIIaq18jVIWmE7P/qf+gLocS1smQPFWbDiFVUWNxDajnJ8Rqp54dB2YxqmTe3HNcxzgNTcEgAiZTE9IZqc01PBs7OzHb7S09NZuHAhffr0YdGiRY3RRiHE2S77MPz9fNVyFwNMeAcu+sC2krBlWvbR9VXrF5i3NfCNapx2noIUc3AT4Seb+ArR1JzuufH3r7oexKhRozAYDDzwwANs3LixQRomhGhG/nnD8bzNKDUEdcnHENNbld20CEoLYN9f8MeDVYOb8hIoyVHHvo2/QJ6zpOdGiDOH08FNTcLDw9mzZ09DPU4I0VzkJKshJ4DQjtBmBIx5oWo9z0D1Fd1Lnadss10rK4J3uqljF3fH1YWB9YeyOJJVRIS/B14GV1JzixneIRyDq9Od01Vomka5UavzWal55p4bCW6EaHIntXGmPU3TSElJ4eWXX7ZuqCmEEFbLXwZTBbQaprZEqIt3qHotzbOVJa2EwhPq2DMQdDq+X5/M8ZwSLk6I5qpP1mI0aQ6PeWRse+4c1uaUm//ULzv4YcNRFt47mFahjuvhmEway/em89vWFBYnqiEz6bkRouk5Hdz06NEDnU6Hpjn+IDnvvPP44osvGqxhQohmYMu3tl6boY/V7x43L/VqLANjBbi4AnY/bwrSWH8oi0d/2g7ARysOYDRphPgY8Pd048CJQgCW7zlxysHN/vQCvl6bbH2efXBzJKuIKV/8x8GMQod74kMcV20XQpx+Tgc3SUlJDud6vZ7Q0FA8POSvFSFEJbvMWyz0uRni+tfvHktwA2rFYhc/KM62q6Ax7eft1rOyChMAD41uz1V9Yzl4ooDhb6xgy5EcSsqNJz0tu7C0gnu+3Ww9r9wztGxPOgczCvF1dyW/tMJaLsGNEE3P6QHpFStWEBERQVxcHHFxcbRo0QIPDw/KysqYPXt2Y7RRCHG2OrFbvXacWP97XN1BZ/7RZNmOoSjLenl7xKXsTy8gxMfAxO5q1lT3FgFM7KGO40O8CfV1p6zCxPZjuSfVbKNJ43/fbmZXim1oLKuozKFORoE6t7wvgL+nm2y7IMQZwOng5oYbbiA3t+oPjPz8fG644YYGaZQQohkoL4Fsc09vaPv636fT2XpvysxDPuaeG2NAS647ogKlpyd05u1JPfj17oHMva0/XgZX8+06useoWZ3bjzoX3Giaxi2zN9D68QUs3Z2Ou6ue89urHKCcojIqjCbeXLyXJYlpZBWqHcmDfdz56qa+tAjy5P+u7+XU+wkhGofTwY1lg8zKjh49Wu00cSHEOSpzP2gmtYeTj5NTty3Bze4/QNPU4n7A9qDR5BoNdIn248Jukej1OrrFBFSZydQlWv0s2mHuuckoKGXuhiMUl9W+yGheSYU1MRjgjSu7M7yD2pohq7CMb9cf4d2l+7h59gbS88zBjbeBwW1D+eeR4ZzXKti5zymEaBT1zrlJSEhAp9Oh0+kYMWIErq62W41GI0lJSYwdO7ZRGimEOAtlHVCvwW0dN8ysD4MXFAKLn1L7Ppl7brZkqCDmsp4xtQ7/dDUHN1uP5gAw+fP/SEzJY196AY9f0LHG+46bt1AAePWyblzYLYrft6nNPLOLypm74Yj1+oq9avZWsI/Buc8mhGh09Q5uLr74YgC2bNnCmDFj8PGxzRowGAy0bNmSyy67rMEbKIQ4SxWkq1ffCOfvtU8q3j5XTSUHDhW6A5AQG1jr7QmxgbjqdRw4Uci+tHwSzbkzC3ek1iu46Rrtz5V9WgAQ6KWCl/+SshzqlpoTmYO8JbgR4kxT7+Dm6aefBqBly5ZMmjRJZkcJIWpnWZfGyR23E4/n4ZGr0cpSYDJaE4oPl6hAIjqg9i0OgrwNDGsfxpJdaczdaNtpPMDLTT3SpKHTUaX3xxLcRAXYfr5ZgpuahPi41/mZhBCnl9M5N1OmTKGkpITPPvuMadOmkZWlfuhs2rSJY8eONXgDhRBnKUvPjXf9g5vMglIuePcfUorsfjSZKqyBUrbJF4OrnuB69JZc0TsGgB8dghsDZRUmxr6zkiv/b02V9bqO5ahVhqPsgqdAbzeHOv8b4biDeH3aIoQ4vZwObrZt20a7du145ZVXeP3118nJyQHg559/Ztq0aQ3dPiHE2SJ1O8y5EtJ2qnNrz01ovR/xzG+JAJTbdyofXQ/5KRhdPDigRRHl74FeX3cOz/AOYYT4GMgqtE3h9ja4sDs1j71pBaw/lE1moeP0bmvPjb8tuAnxcbcOPXWK9GOSebjKIqCOnh0hxOnndHBz//33M3XqVPbt2+cwNHXBBRewcuXKBm2cEOIssv4ztenlX4+rc2vPTd3BzacrD9LysT/4datK3jVQbrtYoXpTDkdPIB8vogPrt+u2m4uei3tEO5QVlxs5lFlkPT+c6bi6sGVdm5Z2C/G5uehZ8L/BPHdxFz66ridRdtsrxAZ54VKPQEsIcXo5Hdxs2LCB2267rUp5dHQ0qampDdIoIcRZKPuQej24HNJ3Q2H9hqVyi8t5YcEu6/mtQ1rhriuvUu83jwlA3fk29q7o7djLUlhawf70Aut5UoYt0MkpKmOf+VrP2ACH+yL8Pbj+vDjigr3R6XQMbhsCwJPja05OFkI0HaeDG3d3d/Ly8qqU7927l9DQ+nc/CyGamezDtuP/PoGC+iUU268rM7JjOPePbIenvsKhTlmLQby3Q+W+XJzg2BtTm/YRvsyY0IlOkX4AFJQaOWAX3Byy2xdq42E13bxVqDfBdSQJvzWpB/PvGsjozicxE0wI0eicDm4mTpzIs88+S3m5+stKp9ORnJzMo48+KlPBhThXmYyQa0vcZcscKDcHDnUs4JeWp4adLu8Vw2dTeuNpcMFD5xjcHGh1HRUmjVYh3gxoHeJU06YOjOe5izsD1fTc2A1LHTRvuNk5qu7FSEN83OnRIsCpdgghTh+ng5s33niDgoICwsLCKC4uZujQobRp0wYfHx9eeOGFxmijEOJMl3ccTOWgd4XQDtY8GYLbgLtPrbfmmPdsCvSyzUrysM+56XwpewMGAmp46GR4u6sE5byScpLsemtSc0usx9nmdsjsJyHOfk7vCu7v78/ixYtZtWoV27Zto6CggJ49ezJy5MjGaJ8QoqmZjHBsI0R0A7caggvLasT+LaDfbfD7/eo8snudj88uUoGM/ayjHe7diSpJpcwQiOGKmaSvPAhAqO/JrSnjbd53KqfIMZcno6DUepxTbGmH49RvIcTZx+meG4tBgwZx55138sgjjzBy5Eg2bdrEhRde2JBtE0KcCdZ9DJ+PgkVP1Fzn0Cr1Gt0Tuk1S+0kBRPbgk5UHuGvOJorKKqq91RJw2C+WNzfodl4pv4rPOn6OpmmcMAchoSe5YJ6l58bCx3x+It8uuDH33AR4SnAjxNnOqeDmr7/+4qGHHuLxxx/n4EH1l9Tu3bu5+OKL6dOnDyaTqVEaKYRoQpap3es/q7nOgWXqtfVwMHjD6BcguhfGzpfx1uJ9/LE9he/XH6n21uqGpfSefnxknMir60pYuS/DGoScdM+Nu4vDuWWDy6IyI9N+3s6inam2IEuGpYQ469U7uPn8888ZN24cs2bN4pVXXuG8887j66+/pn///kRERLBjxw4WLFjQmG0VQpyJspLUsBVAq/PVa8/r4Za/Sa4IoLhc7cT9xeokjCaNxYlppOdXzXXxtwtu7Hta5m8+dsrBjburC24utvVousf442VQAc+3/yVz3/dbOJKtpoX7S8+NEGe9egc377zzDq+88goZGRn88MMPZGRk8OGHH7J9+3Y+/vhjOnaU9R6EaHYsC/FZGKsZWlr1JqBBm5Hg7zhNe3eKbdmII1nF3PbVBm6ZvYGpX6y3llc3LKW32/MpzM/9lIMbAF8PW9DSJszH4VlFZUaOZBVXaYcQ4uxU7+DmwIEDXHHFFQBceumluLq68tprrxETE9NojRNCNLEvJzqe51XaP27tR7BptjoecE+V23el5gNgcFE/apbsUsFSYkoeFUYTmqZZE3ntgwr7WUxocNTcqxLhd/Ib9naLsU3xbhPmU2P+jiQUC3H2q3dwU1xcjJeXF6DWtnF3dycyMrLRGiaEOA0y9sPK16E4p/rrOYcdz7+6BH6YrKZ+7/oNFpr3kxs5A1oNq3L7kSwVlFzfP84a4FjsScsnr7gCo0ltXmkfVOQW22Y1rTmYSWGZET8PV1qF1j6tvDbtwn2tx3HB3jX2AsleUUKc/ZyaCv7ZZ5/h46N+uFRUVDBr1ixCQhwX1Prf//7XcK0TQjSu5S/Cjp8gaSWEtod9i+Hq7yCsA5QWQLl5e4LuV8PWb9WU76wDoGmwbxGgQe+bYOB91T7esmll+whfpgyI49N/kqzXNiXnsHp/BgCR/h54uNmSfh+/oCNXf7oWgG1HcwHoGx90Svs4Xdcvjq/WHKZXXCAGVz3hdr1AQd5qg029DnzdnV4hQwhxhqn3/8WxsbF8+umn1vOIiAi++uorhzo6nU6CGyHOJpZVhZNWqC+AmWPhlr9tddy84OKPYNADaluF9Z/Crl/VtdYjYNyroKs+6LAEN8HeBi4f15HoAE8+XH6A9PxSVu/L4J99aouG+0e1c7ivf+tgXrykK4/P224t6xcffEofNTbYixWPDLOueRMVYAtuJvePY/meE7QK8a7XjuNCiDNbvYObQ4cONWIzhBBNoiCtallxNix4BIY8rM69Q1XwEtoO2o5SwY1Fz8ngon6MlJQbWbn3BOd3CMPNPARlCW4CvQ3o9TqmDozH18ONB+duZeFOtdFu9xh/Lu9ZNXcv2MdxeKhfq6BT/bSE+doCmkh/2wacMYFezL9r4Ck/XwhxZjjpRfyEEM2AZXPL7teoV0tS8P4ltund9htfhjj2sBDTx3r43t/7uPWrjTz3e6K1zL7nxqJtuGPezIyJnavtLfGzm93k4+5q3fyyodj33ARKErEQzYoEN0Kcq8oKbZtbjnsFph2F0c9Dy8GABkueVte87YKbgDjQmXNjPALAP5pNydk8PHcrHyxTWzDMXnOYcqOJ4jKjdY2bILvgprVdUvAVvWJIiA2stnl+nraO5d4tA3F1adgfV/Y9N7K2jRDNiwQ3QpyLNA3mTlXHrp7g7qu+AC58SwU0RtXrgk+o7T69Hi7+UCUYXzUHo0nj/u+3MHfjUYfHL9udTpZ5cT43F511uwNQC/SN7hROXLAXj4ztUGMT7XtuTjXfpjphdrOl7JOZhRBnP5kWIMS5KOugebYTUFHsmBAc0ham/AZfXgiFJ8CvUj5M96vUF7B0ZyqHM4uqPP6HDUfpYl7Az9/TgK5SwvEnk3tjMmm1Ju/62fWmnNcA+TaVubroeWRse45kFdM5qmGHvIQQTUuCGyHORZn7a78e1gFuWAjbvodeUykorcDb4FIlSPlitZrafePAeKIDPfH3dOOhuVtZsiuNJbtUsrKnofoO4rpmJfl5uNK9RQBlFSa6RPvXWvdk3TmsTaM8VwjRtE5qWOrAgQM8+eSTXH311aSnqxVH//zzT3bu3NmgjRNCNJKMfbbjPjdXXyekDQx/giXJJro8/Refr0pyuLzzeC5rD2bhqtdxy5B4bhoUz+W9YujRIsCh3mXVzISqD51Ox7w7BrDgf4Oss6+EEKI+nP6JsWLFCrp27cq6dev4+eefKSgoAGDr1q08/fTTDd5AIUQjyDQHNx0nwqjnaq36v+82A/D8H7scyi3BzgVdIx2Sc6/obQtmRnYM576RlWZYOUGv11XpLRJCiLo4Hdw89thjPP/88yxevBiDwTYDYvjw4axdu7ZBGyeEaCQZ5mGpDheCQW2rsmhnKi8u2GXdDgHU2jVFZUbr+fpDWQCk55Xw29bjANw4KN7h0RO6R1mP7aeACyHE6eJ0zs327dv55ptvqpSHhYWRkZHRII0SQjSSilJwdbfm3CTrIvny90RaBnvx1C9qWLlPyyBGdQoH4KdNjrOgrvh4Dd1i/GkT6kO5UaNXXGCVYSg/DzduHdKKmauTmDKgZaN/JCGEqMzp4CYgIICUlBTi4x3/Wtu8eTPR0dEN1jAhRAMylsNv98H2H2DEdChQqwO/ssHIH3sdc2mSs2yzn75aozbOvGVwPOn5pfy5PZVtR3Ot+z1NrSF4mTauAw+PaS+5MkKIJuH0T56rrrqKRx99lNTUVHQ6HSaTidWrV/PQQw8xefLkxmijEOJUrHobnguBLV+rtWsWPQmA0SuUP/aqQKZ7jG02UlKGyqMzmTQOnlCL/E0Z0JJ3rkrgr/uHWHf3DvFxZ0zniGrfUqfTSWAjhGgyTv/0efHFF+nQoQMtWrSgoKCATp06MWTIEAYMGMCTTz7ZGG0UQpyKnfNsx3GDrIcbCmwL4829fQCvXd4NgKQMFdBkFpZRZjSh02HdQTs+xJtPJvfijmGt+WJqbwyuEsAIIc48Tg9LGQwGPv30U5566il27NhBQUEBCQkJtG3btjHaJ4Q4VYXmXLjLZ0K7sfDFGEjdxl6TmtXUOcoPg6ueVqHeAKzen8mf21OIDFAzoMJ83R16YYa1D2NY+zCEEOJM5XRws2rVKgYNGkRsbCyxsbGN0SYhGp3RpLErJY8OEb4NvmdRk0ndAT/fCv3vhITrVJmmQZE5uInuqWZGXfsj33z0LB9m9QPgqQs7AdAp0p8IPw9S80q4Y84m2pk3uIywm+YthBBnA6d/qg8fPpz4+Hgef/xxEhMT675BiDPQe3/v48L3VlVZu+WsZTLBTzdB+k745S7ISVblZQVQUaKOvc17RPmG81b5JaQQzO/3DOK8Vmp4ytPgwtzb+zO+ayQAe9NU7k2UvwdCCHE2cTq4OX78OA8++CArVqygS5cu9OjRg9dee42jR4/WfbMQZ4i3l6hF7Gb9e6hpG9JQtv8AJ3bbzi37RhWeUK9uXmBQw05lFSYyCkoBiKgUuLQI8uKDa3vywiVdrGWR0nMjhDjLOB3chISEcPfdd7N69WoOHDjAFVdcwZdffknLli0ZPnx4Y7RRCFGb0gJYMsOxLE8tsGfNt/EOwWTSSMktJj2/BE0Dg4ueIK/qF9m7tl8cj47tgJ+HK8M7SH6NEOLsckobZ8bHx/PYY4/RvXt3nnrqKVasWNFQ7RLitGgWK/tv+hLyUyCwJXS7Cla8DHkp6pq556bYLYgpn67lv6QshrRTw1Ph/u61bl55x7DW3D60lWx/IIQ465x0JuXq1au58847iYyM5JprrqFLly788ccfDdk2IRqdv6dbUzfh1GXsBSC37WV8vN2kyvKOqVdzcLM6Vcd/SWrrhJV7VVmkX93DTRLYCCHORk4HN9OmTSM+Pp7hw4eTnJzMO++8Q2pqKl999RVjx45tjDYK0aDs904664MbkxEKVLDy/a5SVqSaP0+OWlmYI/8BcEwLAVRvjEXlfBshhGgunB6WWrlyJQ8//DBXXnklISEhjdEmIRpVZmGp9djH/ZRGZpuWpsHsi+DQPwBszHQlVQtS17IPwY83wW7Vm/qLcSDz7hxAQmwgPu6uvLV4LwPbBNfwYCGEOLs5/ZN99erVjdEOIU6b1NwS63GFUaulZgMyGeHf9yCsI7Qb0zDPTNlqDWwAMjU/UrVA2/UdPwKwV4thk9aWYG93AO46vw03DYrHw82lYdohhBBnmHoFN7/++ivjxo3Dzc2NX3/9tda6EydObJCGCdFYLNsLAJRWGE/Pm+6cB0ueBp9weGhvwzwzcb7DaSb+FOPB3IohXOG60lr+TcVwQEegt20ITgIbIURzVq/g5uKLLyY1NZWwsDAuvvjiGuvpdDqMxvr/sli5ciWvvfYaGzduJCUlhXnz5tX6fHurV69m6NChdOnShS1bttT7PYWwbAYJUFJuavw3NJngnzfUcUEalBVa15w5aZoGO+c7FBW6BnJD35Y8vPp2BvkcI7LkAJqLB/NKBmFw0Z/dQ3BCCOGEeiUUm0wmwsLCrMc1fTkT2AAUFhbSvXt3PvjgA6fuy8nJYfLkyYwYMcKp+8S57VhOMduP5rI/vcBaVnI6em72/QXpdqt55xw59WemboPsJIeiIV1b0yHCF4DtplbqreIvIBcfAr3dZOaTEOKc4fSfcrNnz2bSpEm4u7s7lJeVlfHdd98xefLkej9r3LhxjBs3ztkmcPvtt3PNNdfg4uLC/Pnznb5fnHtKyo1MeG8VWYVlVcoblabBytcdy3KPQFiHU3vugWVVijpH+xNl3uzy2fzxuEYE4NH1AdiRRJC3e5X6QgjRXDk9FfyGG24gNze3Snl+fj433HBDgzSqNjNnzuTgwYM8/fTT9apfWlpKXl6ew5c49+w4llslsAE1LKVpjZhUfHQDHNuA5uLBFlMbVWbZ9+mUnrsegE8qxpOt+TC3YggxgV7WrRKOamHcmHIJqUY/AIK8z/Ip70II4QSngxtN06rt3j569Cj+/v4N0qia7Nu3j8cee4yvv/4aV9f6dTq99NJL+Pv7W79atGjRqG0UZ6bNyTkAjOgQxrw7B/Dj7f2t10orGjHv5tgGAI4G92eLeaioZNvPqkfnZGmaNbhZZOxFn9IPebjiNqIDPIkKcFy75tv/VCAlPTdCiHNJvYelEhIS0Ol06HQ6RowY4RBcGI1GkpKSGnURP6PRyDXXXMMzzzxDu3bt6n3ftGnTeOCBB6zneXl5EuCcY1Jyi3nvb7VRZu+WQSTEBlJutAU0peWmxps9ZN7MMkkfywaTH1NZhMeRVfB2V7j0E4gb4Pwzc49AQRpGXNiutaLC/L9xdKAnXgZX7hzWmg+XHwBg/aFsXPU6ruwd02AfSQghznT1Dm4ss5i2bNnCmDFj8PHxsV4zGAy0bNmSyy67rMEbaJGfn8+GDRvYvHkzd999N6CSmzVNw9XVlUWLFlW7cae7u3uV/CBxbpm/+Th5JRUAjO8aCYCbix4XvQ6jSaOkwog/jTRsk66Cm/VF4fxu6s5I40YudvlXBSjfXg2PHXb6kdqR/9ABO0xxlGLb+NKy2vIjYztwIr+UuRuP4uPuysfX9WJQW1lwUwhx7qh3cGPJcWnZsiWTJk3Cw+P0Lt3u5+fH9u3bHco+/PBD/v77b3788Ufi4+NPa3vE2SMtTy3aN6V/HLHBXtZyD1c9hWXGxksq1jRrz82KrGBAxwcVF6vgBqAkB9J3qYX9jqwHgxeEd671kcVlRrasXEh/YJOprbW8b3yQQ73bhrbG1UXPlAFxdIjwa8APJYQQZz6nZ0tNmTKlwd68oKCA/fv3W8+TkpLYsmULQUFBxMbGMm3aNI4dO8bs2bPR6/V06dLF4f6wsDA8PDyqlAthL6NAbbcQF+y4toyHm4s5uGmknJuSHPUF7KkIB2CfFsMM7VZm6D5RdXbOBw9/+HykOp+eBfrqh8iyC8vo//JSvtVtBj1sNicoP35BByb3b+lQt02YDy9d2rWBP5AQQpwdnE4oNhqNvP766/Tt25eIiAiCgoIcvpyxYcMGEhISSEhIAOCBBx4gISGB6dOnA5CSkkJycgPMLBHntBP5KrgJ8XUcnrTk2TRaz03ecQBK3QIoxUDflkG46nXMKh1G1uh3VZ0VL8ObHW33ZCVV8yBlT1o+WnkJnXWqzqv33czXN/Xj5kGtZMVhIYSw43Rw88wzz/Dmm28yadIkcnNzeeCBB7j00kvR6/XMmDHDqWcNGzYMTdOqfM2aNQuAWbNmsXz58hrvnzFjhqxOLOp0wtxzE+rjGNy4u6p//o0X3KQAkOmi8l16xgXSKlT1Hq1z6wf6avJ80nfW+LjcgmIWGR7BoDOCZyAeofEMahuCXi+L8wkhhD2ng5s5c+bw6aef8uCDD+Lq6srVV1/NZ599xvTp01m7dm1jtFGIU5Jh7rkJ9TU4lLtbem4aayp43jEAjhrVZpbdYvwZ3SkCgDt+OkBqyHlV70nfVePjdKlbidOnq5MW54GsOCyEENVyOrhJTU2la1c1lu/j42Nd0O/CCy/kjz/+aNjWCXGKSsqN1plSoT6OSfAhPirYsSQcNzjzsNSBEpXQ2y3GnxsH2RLff8ytZpXi1O1Vy8wqCjJsJ+fd0TBtFEKIZsjp4CYmJoaUFNXd3rp1axYtWgTA+vXrZcq1OONYkokNLnr8PB3z51uaE4ztdwlvUOaem+OmQIK9DUQHeBLkbWDx/UMAeC93ICXdroPhT6kvgCP/1bjAn7EgE4ADvn2h1dDGabMQQjQDTgc3l1xyCUuXLgXgnnvu4amnnqJt27ZMnjyZG2+8scEbKMSpWHcwC4BWod5VVtaOD1HBzaHGCm7y1R8BqQTRMdLP+v5tw31pE+ZDKQZWtn8KhjwE/e8GFwMUplfZENOqOBsAo3vjrgQuhBBnO6engr/88svW40mTJhEbG8uaNWto27YtEyZMaNDGCXGq/tyRCsCYzhFVrlmCm0bruSlUw0gZmr/D+joAfVoGsT+9gPWHshjdOYIKvYE8/04EZW2B5HUQ1KrK43QlKlDTPJ2blSiEEOcap3tuKuvfvz8PPPCABDbijJOWV8LyPSoB9wLzysT27IMbo6kRNs8sVsFItuZLbJBjcNM3XiUZ/3dI9cY89csO5qZHq4tHqk/Mdy1V+W06r8CGb6sQQjQj9eq5+fXXX+v9wIkTJ550Y4RoSHPWHqbCpNGnZSDtI3yrXI8J9MTdVU9phYnDmYW0CvWp5imnoMgc3OBDi8CqPTcAW4/k8NHyA3z73xFG69sBf1i3V6jMvVwFNy4+wQ3bTiGEaGbqFdxY9pWqi06nw2hspDVDhHBCaYWRb8w7Yk8dUM3WHGWFuO75k55hPqw5DrtT8xs2uCkvgbICoPqem+gATyL9PUjJLeGVhWqLhk0m84aw6bugOAc8Axzu8axQwY1BghshhKhVvYalTCZTvb4ksBFnij+2pZBRUEakvwejO4dXrfDnI/DTTTxd9hYAu1LyGrYBm2YDUKHpycOrSnCj0+msvTcWGfiTZApHhwZH11d5pI8pHwCDr2yCKYQQtTnlnBshzkSz/j0EwHXnxeHmUs0/881fA9ChQOW37ErJI7+knF+2HCO/pPzU3vz4FvjzYQDy8CI+xAd/r6qrEdsPlV3dN5b/DW/DRq29Kkh2zLsxmTT8NEtwIz03QghRG6dnSz377LO1XrfsCyVEUzmWU8y2o7m46HVc1adFve7ZlZLPYz9v549tKYzvGskH1/YE1GaV3u6uGFyd+DvgwN/WwyBdAT1jq08Avjghmg+X7ee8VsG8dGlX/j2Qwa8r2nG5y0q0I+sc8m6Ky40E6NQwl4ef9NwIIURtnA5u5s2b53BeXl5OUlISrq6utG7dWoIb0eRW71NTsLvH+BPsU7+FJY/lFHMspxiAP7an8AGwJzWfCe+tYkL3KN64snu931878LdDYJIQG1BtvegAT9Y+PsIaOCW0COQtnQrGjJlJDv9zFhYVE6ZT7ZPgRgghaud0cLN58+YqZXl5eUydOpVLLrmkQRolxKlYfUAFN4Pa1C8IiA7wtAY29t5espcyo4mfNh2tf3BTmo+WvNYa3Bg1HdEBnjVW9/WwDVd5GlwICo+FTNAVpqmVis0L/xXlZVrr6TxlKrgQQtSmQXJu/Pz8eOaZZ3jqqaca4nFCnJQKo4kHf9jKL1vUnk594uu32F3HyKrTxAGyi8qsx5ZtHGpzx9cbeeH9/0NvKqdcc2GpMYHryh8nyNtQ570WbVq1AcDFVM6RY0et5eX5KmDLwxv0LvV+nhBCnIsaLKE4NzfXuommEE1h/aFsftpkCwi6xQTU676OkX5VyiqMJnal5FvP96bmV6lj73hOMX/uSCU2ew0A3xiHc1P5w6wxdXYquDmvXSRZmpqS/vf6bdbyUvO+Uvm66gMxIYQQNk4PS7377rsO55qmkZKSwldffcW4ceMarGFC1IfRpOGiV0M3q/afcLjm71l1hhIAJsclCzpEVA1ulu05QW5xOZ11Sdzn+hPHDj4DbYbU2I7ktT/zk+Eteun3AbDCZBvGCvapf3AzqE0IB/XBBGkF6IvSrOWWTTMLXaq2VQghhCOng5u33nrL4Vyv1xMaGsqUKVOYNm1agzVMiLrsTcvn0g//ZUznCNpH+PDlv4et124ZXM3CfaDyWEocexg7RlRdvO9b8wKAswyvEKrL48TGu2D09hrbErrjc1qbAxuAtaZO1mMvQ/3/N9PpdLgGREH2YTyK063lpiK1TUORBDdCCFEnp4ObpKQadiwW4jRbtS+DgtIKh6GonrEBvHhpV1qFVApYTCb46iIoK4RxrzlcivN3wdPNheJyW4/O37tVYBGqU4v7hZYm19yQw2toXbDRevqTcRBFeJzsx6LEIwwAr1K7nijzVg4lrhLcCCFEXZwOboRoSiXlRjzcVEKtfcJvqK87D45qx5W9W6DXV7MzU85hSFqpjudOdbjkUl5I+whfthzJcSjv1zIAUu0KirLAq1KScvZhmDnWetqr5CMyObUApMLdHwC3cluej65E9dyUGgJO6dlCCHEucDq4KSkp4b333mPZsmWkp6djMpkcrm/atKnBGieEva/WHGL6rzsZ0ymConIjK/eqno37Rrbl3hFt0emq227S7MRu23FupV6Ysnw6RvpVCW5ua5fvENyUHVyNocsEhzpp/83FfnOHTPyd+ETV07mprRp0FSXWMhdzcFNhOPXnCyFEc+d0cHPTTTexaNEiLr/8cvr27Vv7LxQhGtBnq5LQNFi4M9WhPNTXve5/h+m71GtAnOrFsVdaQKdKM6aivTXOX3WdQ1nB3hUEVQpuTIm/WY8rNDX50FWv47FxHXj+j12M7xZZ18eqQmdQ6+K4GG1r77iWqTyhCndZ40YIIeridHDz+++/s2DBAgYOHNgY7RGiRvbhS5swH/anq+0IQuuzCrGl56bn9eAXAznJsPVbyE6CrAP0bjkCgP6tgnloTDvikuehW6r2mFppGMyQsn9wObLG8Zn5qYTnbgVglbEz71Zcqp7ROpgbB8bTNz6IduHOT912MaieG1ejrefGUJYDgMkjwOnnCSHEucbp4CY6OhpfX1lrQ5xeFUYTR7NVT8bqx4azeGcqM35LBCDEt47gRtPg8L/qOKIbtBujjvcuVMHN3Kl0fPIEv949kJgAT7UuzZ+zVJ2Rz7BgXweGHP4Hv+ydUHCCEvcgPNxcKN3+C+5obDK14dP4t3liVDt8PVwJ9XVHr9fVe52dyvSW4MZkF9yUq54bF2/ZNFMIIeri9CJ+b7zxBo8++iiHDx+uu7IQDeR4TgkVJg2Dq55IPw9ahdpmQ9XZc3N8E+QeATdviLdbq2aY3dIFSSvpVvAvQe+3g6XPQuo2cPWAnpPxCY1lu6klOjRSNvxCt2cW8dKfuyjc8jMAa90HMuuGPnRvEUCrUB+HLRVOhouHNwCuJtuqyJ7m4MbLX/aVEkKIujgd3PTu3ZuSkhJatWqFr68vQUFBDl9CNIakzEIA4oK80Ot1xAZ5Wa+F1tVzs+EL9dpuDLjZ7fPUbjT0vkkd7/oVvrsaSnJg1ZuqrMvl4BVEiyAvlhh7AXB87Y+MMa1i5cpl+KevB6CkzfgGzT1zNffcGDRbcONtUjOnfALDGux9hBCiuXJ6WOrqq6/m2LFjvPjii4SHh0tCsTgtEo+r9WYsOSwtQ7y56/zWeLq5WKeGVyvvOGz5Vh33v6vq9Y4TYMPnsPuPqtf63gJATKAn35t6cT8/0atkDb0MttybA6ZIunbtcVKfqSZulp4bYwnrD2XRJ8YbT9QQlX+QBDdCCFEXp4Obf//9lzVr1tC9ez13SRaiAWw/lgNAtxjbVOiHx3So+8a9C0EzQkwfiOld9XrLQeAZCEUZjuX+sRDVQ1UJ8SZRi+O4FkyULtOh2kGi6d+6YfNgLMGNJ6Vc+PEadj/SAw/UDuPBIaEN+l5CCNEcOT0s1aFDB4qLi+uuKEQD2nZU5Zx0jXFynZd9i9WrJYm4Mhc3aH9B1fKQNtbD1qE+jOgQzmJjzyrVcj1j8HFv2LUwDebgxkOnFilMS1W7nOfig69H/fepEkKIc5XTwc3LL7/Mgw8+yPLly8nMzCQvL8/hS4iGtuNYLkezi3HR6+gS7URwU5gB+5eo43Zja67XcWLVMv8Yh9OnJ3Rmha5qz0++V2z921NPBk+Vc+OBCm6OHFfBTYHeV4aBhRCiHpz+k3PsWPVLYsSIEQ7lmqah0+kwGo3V3SbESfu/lQcBuLBbJH7OzETa/BUYyyAqASK61lyv1TAw+EBZga3M4Lg3VWywFwmDJ3J09afE6GxDWCW+LevfnnoyWIelVHCTdHAfg4AiF1mdWAgh6sPp4GbZsmWN0Q4hqpWcWcQf21TPxa1DWtX/RpPRNkuqz82113XzUMNWO36ylYV1qlLtlvPbc9Ohr/jq2AXodZp6m8Aadh8/BTqDCm68dKWAhkvyKnCF8oiEBn8vIYRojpwOboYOHdoY7RCiWp+tOohJgyHtQukc5UTPxb7FahVijwDofGnd9TtOtAU3/e+G7ldXqeLh5sKcWwfADM1a5h7Uov5tqi+76erulDPEZTsA7Qde1PDvJYQQzZDTwc3KlStrvT5kyJBarwthL7uwjJu+XM/gtqHcP6qdw7XMglJ+2HAEgNur67WpKIXlL0PniyGy0uy99Z+p14TrwOBV5dYqOk6E0S9AdC+I61/v9of41ePZznK1BTdj9BuI0WVg1LthaD244d9LCCGaIaeDm2HDhlUps09ylJwbUV9lFSZu+nI9m5Jz2JScUyW4+XLNYUrKTXSL8a9+uvV/n6oF91a9CTNybeUn9sB+8yyp3jfWrzF6PQy42+nPEFKffa2c5WL73/Jdw/sA6OIGgHm4SgghRO2cni2VnZ3t8JWens7ChQvp06cPixYtaow2imbqxQW72JScYz3XNM3h+i9bjgFwy+BW1c8SytxvO36lJfzzBmQdVL05AB0uhODWDdxqm2LNQJhfIwQ31dC3Hn5a3kcIIZoDp3tu/P2r5j2MGjUKg8HAAw88wMaNGxukYaJ5Kyk38uPGow5lucXlBHipdVzK7TbK7Btfw7YeJXa9NcXZak+opc+qc50ehjzU4O0G+Nw0npv0fzC9Yiovh/rUfUNDaDOi7jpCCCGAk+i5qUl4eDh79uxpqMeJZm75nhMUlFYQ4mPAVa96ZTIKyqzXU3JKMJo03F311W+MuW8J7FQbV9LqfBjzErS0y0k5/3E1BbwRlJ3/DMNK32TAZf/DRX+a1p0J63x63kcIIZoBp3tutm3b5nCuaRopKSm8/PLL9OjRo6HaJZq537aq6d2XJESzdFc6BzMKySwopU2Y6gn5zTz9u4V5o0wHFWUw5zLb+ejn1Do2592hZkl5+EHseY3W9tuHtWFS3ziCvBtxteApv8MfD0DGXuh8icoJEkIIUS9OBzc9evRAp9NVyY8477zz+OKLLxqsYaL5yS8p54aZ64kP8WbJrjQAJnaPZsuRHBXcFKqemx3HcnntL9ULGOHnUfVBu39zPA+IU686ndrpu5HpdLrGDWwA4gfDXf9B0kq1L5YQQoh6czq4SUpKcjjX6/WEhobi4VHNLyEh7Pyy5TgbDmez4XA2APEh3nSJ9rPOOMooKGVPaj5Xf7LWeo+3ezU7fq+3C6JHzlA9Nc2RTgetZF0pIYRwltPBTVxcXGO0QzRTBaUVHMkqomOkHwt3pDpcm9A9Cp1OR7CP6gU5nlPCLbM3kF9aYa1z+9BKs53Sd8PhVSph+L7tVfaAEkIIIeo9kP/333/TqVOnajfHzM3NpXPnzvzzzz8N2jhx9nvm152Me+cfFmxPYXNytrW8TZgPV/dVq/tGBahF675ac4jkrCJrnVcu60pCbKDjAzfOVK/txklgI4QQolr17rl5++23ueWWW/DzqzoE4O/vz2233cabb77J4MGyiqqw2WgOaF77aw+FZWqBx93PjcXDzTbc1MscwFiuW7QMrmbRuh3mGVL1XZxPCCHEOafePTdbt2617ghendGjR8saN8KB0aRxxNwTk5RRCKgEYWtgYzJB6nZ6b36cq12XA9Ax0g8fd1c83PR0jKoUSBdmQGG6OnZiiwQhhBDnlnr33KSlpeHm5lbzg1xdOXHiRIM0SpzdDp4o4M3Fe1mx5wTlRsdZdS2CzPsmGSvg++tg75+4AC+5wo+moTw9oROtQr0pN2r4eVT693bCvI5SQKxsRSCEEKJG9Q5uoqOj2bFjB23atKn2+rZt24iMjGywhomz16sL97BwZ2q111oEmTeaXPQE7P3T4drqm6IJa1XNHlIWaTvVa2iHhmimEEKIZqrew1IXXHABTz31FCUlJVWuFRcX8/TTT3PhhRc2aOPE6XE8p5hrPl3Lr+aF9U5FUVkFy/emO5RFB3gyuG0IAK1DfWDjLFj3sbo46H5rvbDsLTU/OPco/PmwOg5tf8rtFEII0XzVu+fmySef5Oeff6Zdu3bcfffdtG+vfsHs3r2bDz74AKPRyBNPPNFoDRWN5/VFe/j3QCb/Hsgk3NedfrX1ntRh+Z4TlJSbHMrevqoHLQK9+GHDEa47Lw4+vUJdOP9JGPowaBqsfhvSE2t+8P6ltuPOl550+4QQQjR/9Q5uwsPD+ffff7njjjuYNm2adYVinU7HmDFj+OCDDwgPD2+0horGkV9S7rD+zLWfrWPjk6Pw96o5v6o2f1Zay+b+ke3o01JtfPm/EW0h84DauVunh763qEreoeq1OKfmB2cdUK99bobonifVNiGEEOcGpxbxi4uLY8GCBWRnZ7N//340TaNt27YEBgbWfbM4I83bfIyiMiOBXm5kF5VTYdLILCw9qeCmpNzI3+ZtFSwi/O02vSzMhPfMgUlEV/AMUMeW1+JsapRpDm6C2zrdLiGEEOcWp1coBggMDKRPH9nv5mynaRpfrTkMwH0j2/Hu0n1kFpZRZjTVcWf1Vu3LoLDMSKS/B/ePbMfqAxlcnBBtq3Bsg+24y+W2Y48A9VqSU/PDsw6q1+DWNdcRQgghcCKhWDQ/65Ky2JdegJfBhUt6RmNwVf8cyiu0Ou6snmVIakznCK7s04J3rkrA3dVub6iMfeo1biD0v9tWbu25yan+wSaTLbgJanVSbRNCCHHukODmHPbv/gwAbmqdh9+n/blMWwxAmdFY223VKqswsThRBTfjukRUXynTEtwMAL3dP73KPTfGclj+Mhxapc6zk6CiBFw9bDuACyGEEDWQ4OYcllNcjisVTE57BTL3Md6oZiSVVtQ8LPXBsv0MfPlv68rDFv8eyCCvpIIQHwO9zQnEVWTsV6/BldZK8jTnbBVnq16axdNh+Uswa7wqT9miXsM7g8tJjaQKIYQ4h0hwcw7LKy7nFpcFhBapoCPKpHpeymoIbjRN47W/9nAsp5i5G444XJu78SgAF3SNxEWvq/4Ns5PUa5XgJkC9mirgu6th7Ye2azlHIGWrOo7sXr8PJoQQ4pwmwc05zJCXzL2uP1nP/bU8/CioMbix7A8F4O1u60HJLylnkXlF4kl9WlT/ZpoGBebF/XwrDVu5eYHePDtr70LHa3sXwq7f1LEEN0IIIepBgptzWM/cRXjoyskK6Qs+ao2ilrq0GmdLLdtj2zvMfqG+zck5lBs1YoO86Bzlrwq1SknJJblgKlfHXiGO13Q6W+8NQMJ10M68SevCaSqZ2C8aOk50+jMKIYQ490hwcw7zLlMJxYVR/SBITbFurTtu7bn5eu1hhr62jMWJaZzIL+XDZfut9+aVlFuPNx5W69P0jjPnzuz4GV6IgD12vTBFmerV4ANuHlUbY8m7iR0AE96FIY+oc1M56F3hilngVUMujxBCCGFHgptzmFdFLgAu3iEQ0wuAS13+sQY303/ZweHMIm6ZvYE+Lywhs7DMeu/CHalkm883JavgJsES3Kx6S81u+nYS7JyvygpVIIV3pV4bi56TIaYPXPIx6F0gKkHt/g0w6jlo0beBPrUQQojmToKbc5iPSQU3Bv8w6HMLJnQMdtmBvkgNP9nn1ViM6qSGr47lFHPd5+sAOJSpcnE6RvjCib2Qus12w9wpaoiq0DykVXlIymLAPXDzEgg0T/XW6+GauXDlV3DeHaf6UYUQQpxDJLg5R6XnlxCg5QHg4R8GgXGU6L3UxVJV7lpp1lOPFgFc1CPKer7zeB6appGWVwpAuJ8H7Pix6pu93hYS56vjmnpuqhPWATpNVDk5QgghRD1JcHOOuuj91QTp8gHw8lcbV5p0asaSsbyUCqOJnGKVV/PznQO4ZXA8n1zfC39Pxz2nsovKrcNY4b7usH1u1TcrPGErdya4EUIIIU6CrIh2DiosrSAlt5hAdxXc6H1UcFOhN4ARTOWlZBeVo2mq06R7TAA9Y1U+TWpeicOzUnPVebC3AUP6VjWzydUTKoqrf/OahqWEEEKIBtKkPTcrV65kwoQJREVFodPpmD9/fq31f/75Z0aNGkVoaCh+fn7079+fv/766/Q0thlJyijEj0Jcdebp3J5qFpJJp2JdraKULHOycICnm8OifJV7btLMwU64nwdsNw9JtR8HPa51fNM+t6j1bOKHNPTHEUIIIRw0aXBTWFhI9+7d+eCDD+pVf+XKlYwaNYoFCxawceNGzj//fCZMmMDmzZsbuaXNy8GMQl50+wIAk6uHdWq2SW9Qr+VlZBaoPJpgH3eHeysHN0ey1TYMkX5usMO8IGDXK+CiDyC4ra3i+Ndh2lFoM6LBP48QQghhr0mHpcaNG8e4cePqXf/tt992OH/xxRf55Zdf+O2330hISGjg1jVfSekF3OuyFgC9efE+AJNllWBjqXXad5C3weHeAC8DNw+K57NVaiuFHcfUjKvzXPZAQaraBLPNSDWe5eoYGKF3QQghhGhsZ3VCsclkIj8/n6Cgmhd3Ky0tJS8vz+HrXJeakmw7uX6e9dDkogIZraKMlXtPMMXlL/7vxPWQluhw/5MXdsLLoAKVBdvVtgtjypeoi50uAldzQHTB62ooauwrjfRJhBBCiKrO6uDm9ddfp6CggCuvvLLGOi+99BL+/v7WrxYtatj76BySdWwfAKVekRDc2lqumYeltienM3fjUZ5x+5LAihPwUX/49z2HZ/h6qE6/gtIK/F3LaZFqDm7sc23i+quhqPNub8RPI4QQQjg6a4Obb775hmeeeYYffviBsLCwGutNmzaN3Nxc69eRI0dqrHsuyCgoxS3/GAAuQXEO1zRzz01ZaWnVGxc96XDq62HLvZnUshhdeRF4BVddSViGooQQQpxmZ+VU8O+++46bb76ZuXPnMnLkyFrruru74+7uXmudc8XCHam8s3QfQ3VqtWDXGoIbd105bcN80IhBl3fUroIGR/6D5DUEGHpYi8dF5MFRILSDLLgnhBCiyZ11wc23337LjTfeyHfffcf48eObujlnhZyiMmb9e4i3l6jhqCnu5k0sLXs3WZiDGzeMXN03Ft2qAsfrm2bDb/8DYEDIM2ygLV4GF7oYVN4NIW0RQgghmlqTBjcFBQXs32/baTopKYktW7YQFBREbGws06ZN49ixY8yePRtQQ1FTpkzhnXfeoV+/fqSmql+qnp6e+Pv7N8lnOBvcMnsD6w+pzS0vTYjmslITHAT8K+UfmYMbA+XEBXlat2Gw+uMB62G4Xi0AOKJjOG7Z5v+GIe0bpf1CCCGEM5o052bDhg0kJCRYp3E/8MADJCQkMH36dABSUlJITrbN7Pnkk0+oqKjgrrvuIjIy0vp17733Nkn7zwZGk2YNbADuH9UON8tQU409NxW09NNAMy/yF9ZZvZoqrFV7RehpHerNbUNaQZaaFk5wm0b5DEIIIYQzmrTnZtiwYWiaVuP1WbNmOZwvX768cRvUDNlvl3BxjyhaBHpCrjmpulJwU2r+52CggmhPta8Uelc13JS+U03rbtEPDi6jg3cRSx8YqnJsCtJUXd+IRv88QgghRF3O2tlSon4OZxYCEB/izdtXJUBRJpSrVYXxj3GoW2EJbnTleFSY8208/KHdWLVf1Pg3IbqXKl/zvppBZTJCUYYqs1sQUAghhGgqEtw0c0eyVCATG+SlCnIOq1ffyCorCMeHq8UQh7X2t+XbuPtBj6vVejU9rgYPP9sNa95XwZJmAnRqKrgQQgjRxCS4aWY0TaO4zGg933ZUbY8QF+wFh9fAPPOCekGtqtzr5q72mOoe6QUl6j48zInaLq6O5xYF6erVK9hWRwghhGhC8tuomXnwh60s3JnKX/cNYVdKHnPWJRNAPnelPQ0zzasI+4TDyBlVbzYnFGMsgxJzz419Tw2onhx7lnwbGZISQghxhpDgppn5ebNaffj+77ewK0UFKD+EzSL8+BpVIeF6GP0ceAZWvdkS3FSUqgRiUBth2qvcc1OoFgTEJ7QBWi+EEEKcOglumpGU3GLr8YbDavp3/1bBtM3erQonfQ0dJ9T8AEtwk7IV0nep404XOdapHNzkmxfw8655CwwhhBDidJKcm2bi4IkC+r/0t0NZTKAnH1zdDV2xeZ2b2P61P8SSYJy6DUzl0G4cdLnMsU7l4Obwv+o1sOXJNVwIIYRoYBLcNBPfr3fcENTTzYVPJ/cmCLWSMDp99UNR9lxsm2Hi4Q8XvlV1ryiDj+P5gaXqtc2Ik2i1EEII0fAkuDlLHckqIr+k3HqeU1TucH3BvYPpGOlny4nxDKp7h24Xu6nh/e4Av8iqdbxDwN2u98ZUofJyons7+QmEEEKIxiHBzVlmzYFMrv98HYNfXcbUmeut5btSHfeBig/xVgeWBfa865Hwa8m5AQhtV0MdN3hgp2OOTZsRMg1cCCHEGUOCmzNQel4Jf2xLwWRy3Jpi4+Esrv50Lf/sUwGLZTZUudHE7pT86h9WaAluQup+Y1e74Ka2HBp3X/C1m/rddnTdzxZCCCFOEwluzkAvLtjFXd9sYvqvOxzKE80BTLtwlfdSVGakqKyCvWn5lBlN1novXdrVdpMzwY1mewaB8bXX/f/27jwqyvve4/hnBpgZhmFYArIo4BY3bFBJMKhX6xYj0eScmvY0PY2muWmC6ZYaNUl7NbdJbGKqpeYajdceozE1aa3GtiZGjUutVmNBEWPFJaAQWYIKDMO+/O4fDwyMzIDJnZnn+Q2f1zkcYH4z8sZl+PrMs3Q9380Q7m9DRETaweFGg3bllgAA3j1RhKaWzoGjtEo51PvewXfAFKT80d2wN+HcNWULTvrgO5DzXzPwSFqXC2J27HNjvo3hpuOwbqD3nY87hpv4cTzHDRERaQqHGw36jyg7Hg3YByOaUFbdeVXv0vaP48ODcUeIsvNvhb0Rn5Uol0r4xoAw3GFxvl4UKtrPcRPWv/cvPGiy8t4S0/0oqVt1bAkadn/vvy4REZEPcS9QDXrDvhgRQTZE66pga5jmuL2kfctNXJgJUaFGXKuqxw17E85eU4ab5PhbLo3Q0gQUHFY+HvzN3r9wTDLw9KdAaGzv9528WNkvZ/xTvd+XiIjIhzjcaFAElJeZputPo7q+8xDvkvYzEMeHByPaouz8W25rcOxYPLr/LSfYKz4BNNmVI6ViU27vi/cbcZuRA5UBh4iISGP4spTGtHY5QkqPNtjaz2XT1iZQXt0IQNly0/Gy1MnCm2hobkOIIQCD7ghx/sUu7VPeD50J6PlHTUREfQN/4qngb2dKkFtc5XItp/2aUAAQgDbY6lsAAMWVdWhqbYMhUI9YqwlRocqWmz2flQIAkuPDoNffsp/MpfargN85w7PfABERkYZxuPGxggo7fvLeafzg7ZNo7nL4NgB8XmHHIxuOOj4PaN9yc/nLGkz5zWEAymHggQF6xFhNAIDmVmVLz/SRt1y4sqoYqDivXHZh8FTvfUNEREQaw+HGx6617xRcWdeMTwtuOq29eegywmF3fG5AC2z1zVj58QXHbcNjlJ2G48KCnR770Jhbjoa6vF95PyANMEd6Kp+IiEjzuEOxj92sbXJ8/PG5Uky6UzmkuuhGHf6SW4JBus4zDUfoamBraEG5rfNw8I6Xo+LCTI7b+ocHIzbMBHyRA9jLgcpCoPAfyiJfkiIioj6Gw40PFV6vRUlV56Cy71w5XnpwNPR6HdYdvozWNoEJAecc6xZdA2rt1fj8y86tOQvSBwJwHm4SI83AtRzg952HjTvw0ghERNTHcLjxkZyrNzFv/XGn276saUTuF1UYEB6MHae+QDQq8VLQFqf7ZJ+7hNrWKFiMgchdPhOBAcoriZEhndeBCjEGAvkfdf+ilhgg9i7PfzNEREQaxn1ufGTHqWuOj+/T/wsjdVcBAHvPlSHvi2o0twp8N+Jit8eZ2moRoNfhVw8mK4NNWxsgBHRdziBsNgQArU3dHouhM3s/0zAREZGf4XDjI8FBATCiCYm6cvyvIQvvBq8GILDvXDm+qKwDAEzRn+n2uFhjI7Y+noZ5qQOAyivAyiRg9XDgg0w8O8qGQL0OP50+FKiv7PZYDOelEYiIqO/hy1I+Et9agtPGp1AolEsb3NF2HYN1pSi4Ho8L5XYEoBXJDTnKnR/fB+x9AbiWg98+NBgRQ9uv43TlKNBoU97OvIcfR57EguWfwmoKAqqKun9RHgJORER9ELfc+EjaF2/DrGtEsv6q47bxeuWiludLbUjRfY7g1hrAFAb0T1XeA4jQ13f+IjXKCfsQMxoAoKspUwYboPtw89hHgNHinW+GiIhIwzjc+EhLl8sqdJgQpJy/Jre4ClMC8pQbB08FAgIBY/tFMBtsnQ+oKVfeJ4xX3jfXAq3NQFsrUP2F8y8+cKIn84mIiKTBl6V8pKm1+3BzD/IdHzv2t7lzpvLe1D7cNFZ3PqBjy0308M7b6quA1kagrfMCm0jiYENERH0XhxsfaWrtflssKtAfFaiFCXfpC5Qbh0xX3re/LIWGaqCpFjj9LnDtlHKbNR4whimDT30lUFuh3G6wAJN+Doz9vne/GSIiIg3jcONNX+YD1y8CI+cCLY0u75Kmz0clLNBDAFHDAWucsmDsGG5swMEVwIk3Ox8UGgcEtw83DVWd+9v0TwUmL/be90NERCQBDjfetH0BUJEPPPg/MLbUOK/pAgDRijR9PgpE+0ATM6pzveNlqYZq4NJ+58daYgBTOIAi5WWpqvadlCOSvPBNEBERyYU7FHuLvUIZbABg338hruWWHX4T7wWgbLkZqitRbovqsi9Nx8tSjTZlB+MOAUYgNBYIDlc+77rlJjzRo98CERGRjLjlxluuZXd+3FCNBFQ7rw+ZBlw9hiH6UgzRu9hRuONoqeuXgZbO61HBGg8EBLVvuYGy5aayfctN+EAPfgNERERy4pYbLxFfKMPN6bahaBEufpsjBqL11t/+fl1elkpIU/a7qS4Car/svN0So7znlhsiIiKXONx4SV2VMpD8vS0F7wXM6X4HoxV1AdbOz+PHAf1GdH4eEgX8514grMvAog8EMl5XPu7YcvPPtcoABHCfGyIiInC48ZoaWxUAIMRixbxFa1FhHeV8h0Aj7PrQzs9n/Hf3X6TfSOCHB4DkbwHTlgHPFwNxKcqa+Q7lfdfz4IT081g/ERGRrLjPjZfU1SpnFraGhcNsCYP5mWPKFbrfGAvYy4F+o9AUFAY0FysPMEe6/oUs/YBvv939dmt899v0nFWJiIg43HhJW2MtAMAa2n7UU8fg8dQRZQdhSzTiYmKBws+U2zu2xNyu0Fjnz5Mm/T9qiYiI/AeHGy/RtygXvAwMDnVeMFkBKPvaGAyGztuD3Wy5cSf0li038zZ+xUIiIiL/xNcxvCSwpQ4AYDD3cGVu0eV6U0Gmr/YFOs5kDAApj7h+mYqIiKgP4nDjJUFtypYbY7DV/Z1E29f/AoaQzo91/GMkIiLqwJ+KXmJsH25MIaE93Kv7lcK/3hfr6WsQERH1LRxuvMQolLMKm0N72HIz+JvK+wDj1/si05cDEYOAic98vccTERH5Ie5Q7A1CIFg0AjogJCTM/f3SnlReXho0+et9nf94VnkjIiIiBw43XtDYYIdRp7zkZOlpy01AEJD6mG+iiIiI+gi+LOUFZwtKHB+HWnoYboiIiMjjONx4mBACh7atAgDUCwP0gdw4RkRE5EscbjystrEZS4L+BAAI1jWpXENERNT3cLjxsJvXv1Q7gYiIqE/jcONhtuulaicQERH1aRxuPKy2srzzk8f3qRdCRETUR3G48bCGamW4uWxMBhLHq1xDRETU93C48bDWGmWfm0bjV7zKNxEREXkEhxsPE7XXAQCtwXeoXEJERNQ3cbjxsIB6ZbiBOUrdECIioj6Kw42HBTVWAgACQqNVLiEiIuqbONx4mLn5JgDAYO2ncgkREVHfxOHGwyytVQAAc0SsuiFERER9FIcbD2pqaUO4sAEAQu/gcENERKQGDjcedMNejwjUAAAsEXEq1xAREfVNHG48qPJ6BQJ1bQAAvYVHSxEREalB1eHmyJEjmDt3LuLj46HT6bBr165eH3P48GGMGzcORqMRQ4cOxebNm73eebtqbpYo73UWICBI5RoiIqK+SdXhpra2FikpKXjzzTdv6/6FhYV44IEHMHXqVOTm5uKZZ57BE088gb1793q59PbUtV9Xyh4Qrm4IERFRHxao5hefPXs2Zs+efdv3f+uttzBo0CCsXr0aADBy5EgcPXoUWVlZmDVrlrcyb1uzTbn0Qn1QhMolREREfZdU+9wcP34cM2bMcLpt1qxZOH78uNvHNDY2wmazOb15S0tNBQCg2cTrShEREalFquGmrKwMMTExTrfFxMTAZrOhvr7e5WNeffVVhIWFOd4SEhK81qerUy690MbrShEREalGquHm63jhhRdQXV3teCsuLvba1wpsuKF8EMJLLxAREalF1X1uvqrY2FiUl5c73VZeXg6r1Yrg4GCXjzEajTAajb7Ig6H9ulJBvPQCERGRaqTacpOeno4DBw443bZ//36kp6erVOQspEW5rpQxLKaXexIREZG3qDrc2O125ObmIjc3F4ByqHdubi6KiooAKC8pzZ8/33H/zMxMFBQUYOnSpcjPz8e6devwpz/9CT//+c/VyHfS1iZgba0GAFgiOdwQERGpRdXhJjs7G2PHjsXYsWMBAIsWLcLYsWOxfPlyAEBpaalj0AGAQYMG4cMPP8T+/fuRkpKC1atX4/e//70mDgOvqm9GpE45EssSyetKERERqUUnhBBqR/iSzWZDWFgYqqurYbVaPfbrllXWot+aAdCjDXj2IhDKrTdERESe8lV+fku1Q7GWxRrqASjXlYKZ57khIiJSC4cbT2ltAgZNUd7zulJERESq4XDjKdZ4YMFf1a4gIiLq86Q6FJyIiIioNxxuiIiIyK9wuCEiIiK/wuGGiIiI/AqHGyIiIvIrHG6IiIjIr3C4ISIiIr/C4YaIiIj8CocbIiIi8iscboiIiMivcLghIiIiv8LhhoiIiPwKhxsiIiLyKxxuiIiIyK8Eqh3ga0IIAIDNZlO5hIiIiG5Xx8/tjp/jPelzw01NTQ0AICEhQeUSIiIi+qpqamoQFhbW43104nZGID/S1taGkpIShIaGQqfTqZ3TI5vNhoSEBBQXF8Nqtaqd0yOZWgG5etnqebJ0Amz1Jpl6ZWoFvNMrhEBNTQ3i4+Oh1/e8V02f23Kj1+sxYMAAtTO+EqvVKsVfZkCuVkCuXrZ6niydAFu9SaZemVoBz/f2tsWmA3coJiIiIr/C4YaIiIj8CocbDTMajXjxxRdhNBrVTumVTK2AXL1s9TxZOgG2epNMvTK1Aur39rkdiomIiMi/ccsNERER+RUON0RERORXONwQERGRX+FwQ0RERH6Fww0RERH5lT53hmKZNDc348qVK+jXr99tn5XRF3JycpCamqp2xldWUFCAo0ePorS0FHq9HoMHD8bMmTOlOttnZWUl/va3v2H+/Plqp/RKS61CCFy5cgUJCQkIDAxEU1MTPvjgAzQ2NiIjIwNRUVFqJ7pVWFiIy5cvIy4uDqNHj1Y7x2HHjh2YPXs2zGaz2ilfycGDB7s9Dzz44IO488471U67beXl5diwYQOWL1+udsptqa2tRU5ODiZPnuy7LypIE1auXCnq6uqEEEK0tLSIZ599VhgMBqHX60VgYKD4wQ9+IJqamlSuVOh0OjFkyBCxYsUKce3aNbVzemW328XDDz8sdDqd0Ol0Qq/Xi9jYWBEQECAsFotYu3at2om3LTc3V+j1erUzbotWWvPz80VSUpLQ6/Vi6NChoqCgQKSmpoqQkBBhNptFVFSUuHjxotqZQgghFi5cKGpqaoQQQtTV1Yl58+YJvV7v+Hs7depUx7radDqdsFqt4oc//KE4ceKE2jm9Ki8vF2lpaY7nVL1eL1JTUx3PBUuWLFE78bZp5d/W7VKjly9LacQLL7zguGJ5VlYWNm3ahLfeegtnz57F5s2b8eGHHyIrK0vlyk7Tpk3DmjVrkJSUhDlz5mDXrl1obW1VO8ulRYsWobS0FHl5ebh48SK+9a1vYf78+bDZbFizZg2WLl2Kbdu2qZ0JQLnYXE9vHX9HtECW1ueeew4pKSnIzc3FnDlz8MADD2DAgAGorKzEzZs3kZ6ejpdeekntTADAhg0bUFdXBwB4+eWX8emnn+KTTz6B3W7HkSNHUFRUhBUrVqhc2Wnx4sXIzs5Geno6Ro8ejd/97ne4ceOG2lku/fSnP0V8fDwqKytht9vx9NNPIzk5GaWlpdi3bx82bdqENWvWqJ0JAMjLy+vx7cKFC2onap9PRylyS6fTifLyciGEEGPHjhUbNmxwWn/33XdFcnKyGmnddLQ2NzeLP//5zyIjI0MEBASImJgYsXTpUnHhwgW1E51ERUWJ7Oxsx+c3b94UJpNJ1NbWCiGEWLt2rRgzZoxaeU46/ofu7q1jXQtkaY2OjhanT58WQihb8XQ6nfjHP/7hWD927JhITExUqc5Z1+eB0aNHi23btjmt/+UvfxHDhg1TI62brq3Z2dli4cKFIjw8XBiNRvHtb39b7Nu3T+VCZ1arVXz22WeOz+12uwgKChLV1dVCCCG2bt0qhg8frlaek45/Ox1bm7u+aenfVoeIiIge36xWq897uc+Nhuh0OgBAUVERJkyY4LQ2YcIEFBYWqpHlVmBgIObNm4d58+bh2rVr2LRpEzZv3oxVq1Zh4sSJOHLkiNqJAICWlhan/WosFgtaWlpQW1sLs9mM++67D4sXL1axsFNoaCh++ctfYvz48S7XL126hKeeesrHVa7J0mq32xEZGQkACAkJQUhICOLi4hzrCQkJKC8vVyuvm47ngbKyMtx1111OaykpKSguLlYjq0epqalITU3Fb3/7W2zfvh2bNm3C/fffj8TERM08bxmNRsfvLQDo9Xq0traipaUFgPIce+XKFZXqnEVGRuL111/H9OnTXa6fO3cOc+fO9XGVe42NjVi4cCG+8Y1vuFy/evUqfvWrX/m0icONhmzcuBEWiwUGgwE3b950WqupqdHMNUW6PkF06N+/P5YtW4Zly5bhwIED2LRpkwplrt1zzz1Ys2YN1q5dCwBYs2YNoqOjER0dDUD54WexWNRMdBg3bhwAYMqUKS7Xw8PDITRyxRRZWuPj41FUVITExEQAwOuvv45+/fo51isqKhAREaFWXjfLli2D2WyGXq9HSUkJkpOTHWs3btxASEiIinWdXD0PmEwmPProo3j00Udx+fJlvP322yqUuTZp0iQsX74cW7ZsgcFgwC9+8QsMHjzYMfhq6e9BamoqSkpKkJSU5HK9qqpKE/+2OowZMwYJCQlYsGCBy/UzZ85wuOmrEhMTsXHjRgDK/zBOnTrltGf5oUOHMHz4cLXynPT2j2r69Olu/8ehhtdeew0zZ87Ejh07YDAYUFZWhi1btjjW//nPfyIjI0PFwk7f+973UF9f73Y9NjYWL774og+L3JOldcaMGcjPz8ekSZMAAAsXLnRa37dvn2NQU9vkyZMd+1OMGjUKV69edVr/6KOPnIYdNfX2PDB06FBN7R+0atUq3HfffQgPD4dOp0NISAi2b9/uWD9//jwee+wx9QK7yMzMRG1trdv1xMRETQ2ODzzwAKqqqtyuR0ZG+vyoSV44UxInTpyA0WjE2LFj1U7B3//+d0ycOBGBgfLMxqWlpdi9ezcaGxsxbdo0jBo1Su0k0ojCwkKYTCanl6q0qqCgAAaDAQMGDFA7BVevXkViYqLLLThaVVdXh6NHj6KpqQn33nuvpk8BQP8/HG6IiIjIr8jzX+8+oKmpCbt27cLx48dRVlYGQNm0P2HCBDz00EMwGAwqF3aSqRWQr9cdrZ286/r169i0aZPL39fHHnvMsV+T2mTpBNjqTbL1ulNcXIwXX3xRU/s2au05lltuNOLy5cuYNWsWSkpKMH78eMTExABQfph9+umnGDBgAPbs2YOhQ4eqXCpXKyBfb0/OnDmDcePGaeKcQv/6178wa9YsmM1mzJgxw+n39cCBA6irq8PevXtx9913s/M2sdV7ZOvtiZaeBwBtPsdyuNGImTNnIiQkBO+88063ywHYbDbMnz8f9fX12Lt3r0qFnWRqBeTqzcvL63E9Pz8fjzzyiCae1O69916kpKTgrbfe6rbfhRACmZmZyMvLw/Hjx1UqVMjSCbDVm2Tq/etf/9rjekFBAZ599llNPA8AGn2O9elZdcit4OBgcfbsWbfreXl5Ijg42IdF7snUKoRcvTKdvMtkMonz58+7XT9//rwwmUw+LHJNlk4h2OpNMvX29DzQ9flAK7T4HMvLL2hEeHh4jyeQunLlCsLDw33W0xOZWgG5eiMjI7Fx40YUFhZ2eysoKMDu3bvVTnSIjY3FyZMn3a6fPHnSsXlaTbJ0Amz1Jpl64+LisHPnTrS1tbl8O3XqlNqJTrT4HMsdijXiiSeewPz587Fs2TJMnz692+vBr7zyCn7yk5+oXKmQqRWQq1emk3ctXrwYTz75JHJyclz+vm7cuBGrVq1SuVKeToCt3iRTb2pqKnJycvDQQw+5XNfpdJp5HgA0+hzr0+1E1KPXXntNxMXFOV2zR6fTibi4OLFy5Uq185zI1CqEPL07d+4UW7dudbt+8+ZNsXnzZh8W9ez9998X48ePF4GBgY7N5YGBgWL8+PHij3/8o9p5DrJ0CsFWb5Kl98iRI2LPnj1u1+12uzh8+LAPi3qntedY7lCsQYWFhU6H0g0aNEjlIvdkagXk65VFc3Mzrl+/DgCIiopCUFCQykWuydIJsNWbZOuViVaeY7nPjQYNGjQI6enpaGtrQ3x8vNo5PZKpFZCvFwCOHTuGxsZGtTN6FBQUhLi4OBw+fBhNTU1q57glSyfAVm+SrRcA3nvvvR4vyaAVWnmO5ZYbDbNarcjNzcXgwYPVTumVTK2AXL1s9TxZOgG2epNMvTK1Aur3csuNhsk0d8rUCsjVy1bPk6UTYKs3ydQrUyugfi+HGyIiIvIrHG40bMOGDZo570JvZGoF5OqVqXXPnj3o37+/2hm9kqUTYKs3ydQrUyug/vMW97nRqI4dSI1Go8olvZOpFZCrV6ZWIiKt4JYbDdm/fz8yMjIQEREBs9kMs9mMiIgIZGRk4JNPPlE7z4lMrYBcvTK19uT8+fNS7PwoSyfAVm+SqVeLrWfOnMErr7yCdevWOQ6z72Cz2fD444/7tIfDjUZs2bIFGRkZCAsLQ1ZWFnbv3o3du3cjKysL4eHhyMjIwNatW9XOBCBXKyBXr0ytvWlqasLVq1fVzuiVLJ0AW71Jpl6tte7btw9paWl4//33sXLlSowYMQKHDh1yrNfX12PLli0+beLLUhoxbNgw/OxnP8OPfvQjl+vr1q1DVlYWLl265OOy7mRqBeTqlal10aJFPa5XVFRg27Ztql+5WJZOgK3eJFOvTK0AMGHCBEydOhUrVqyAEAK/+c1v8PLLL2P79u24//77UV5ejvj4eJ/2crjRCJPJhDNnzmD48OEu1y9cuIAxY8agvr7ex2XdydQKyNUrU2tAQADGjBkDq9Xqct1ut+PUqVOqPwHL0gmw1Ztk6pWpFQDCwsJw6tQpDBkyxHHbtm3b8OSTT+L999/HPffc4/PhhteW0ohx48aJJUuWuF1funSpGDdunA+L3JOpVQi5emVqHTZsWI/XwTp9+rTQ6/U+LHJNlk4h2OpNMvXK1CqEENHR0SI7O7vb7e+9954wm81i/fr1Pu/lVcE1YvXq1ZgzZw4+/vhjzJgxo9tVVQsKCvDhhx+qXKmQqRWQq1em1rvvvhs5OTn4/ve/73JdK1culqUTYKs3ydQrUysAjBkzBocOHUJqaqrT7d/97nchhMCCBQt83sSXpTTkypUrWL9+PU6cOOF04bH09HRkZmZi4MCB6gZ2IVMrIFevLK1lZWVobGxEUlKS2ik9kqUTYKs3ydQrUysAfPDBBzhy5AiysrJcrm/btg0bN2502snY2zjcEBERkV/hoeBERETkVzjcaMi6deswY8YMfOc738GBAwec1q5fv66pkzbJ1ArI1ctWz5OlE2CrN8nUK1MroL1eDjca8cYbb2DJkiUYMWIEjEYjMjIy8OqrrzrWW1tbNXPSJplaAbl62ep5snQCbPUmmXplagU02uvTY7PIrVGjRok//OEPjs+PHTsmoqOjxbJly4QQQpSVlWnm0D+ZWoWQq5etnidLpxBs9SaZemVqFUKbvRxuNCI4OFgUFhY63Xb27FkRExMjnn/+eU39ZZapVQi5etnqebJ0CsFWb5KpV6ZWIbTZy/PcaERUVBSKi4udDvMdPXo0Dh48iGnTpqGkpES9uFvI1ArI1ctWz5OlE2CrN8nUK1MroM1e7nOjEZMmTcLOnTu73T5q1CgcOHAAe/bsUaHKNZlaAbl62ep5snQCbPUmmXplagW02cstNxrx/PPPIycnx+VacnIyDh48iB07dvi4yjWZWgG5etnqebJ0Amz1Jpl6ZWoFtNnLk/gRERGRX+GWG405efIkjh8/3u20+2lpaSqXdSdTKyBXL1s9T5ZOgK3eJFOvTK2Axnp9uvsyuVVeXi4mTpwodDqdSEpKEmlpaSItLU0kJSUJnU4nJk2aJMrLy9XOFELI1SqEXL1s7budQrDVm2TqlalVCG32crjRiHnz5on09HSRn5/fbS0/P19MmDBBPPzwwyqUdSdTqxBy9bLV82TpFIKt3iRTr0ytQmizl8ONRlgsFnHq1Cm369nZ2cJisfiwyD2ZWoWQq5etnidLpxBs9SaZemVqFUKbvTwUXCOMRiNsNpvb9ZqaGhiNRh8WuSdTKyBXL1s9T5ZOgK3eJFOvTK2ARnt9OkqRW08//bRISkoSO3fuFNXV1Y7bq6urxc6dO8XAgQPFj3/8YxULO8nUKoRcvWz1PFk6hWCrN8nUK1OrENrs5XCjEQ0NDSIzM1MYDAah1+uFyWQSJpNJ6PV6YTAYxMKFC0VDQ4PamUIIuVqFkKuXrX23Uwi2epNMvTK1CqHNXp7nRmNsNhtycnKcDqVLTU2F1WpVuaw7mVoBuXrZ6nmydAJs9SaZemVqBbTVy+GGiIiI/Ap3KNaQ+vp6HD16FP/+97+7rTU0NOCdd95Roco1mVoBuXrZ6nmydAJs9SaZemVqBTTY69MXwcitCxcuOE54pNfrxeTJk8W1a9cc61q6xL1MrULI1ctWz5OlUwi2epNMvTK1CqHNXm650YjnnnsOo0ePxpdffokLFy4gNDQUkyZNQlFRkdpp3cjUCsjVy1bPk6UTYKs3ydQrUyug0V6fjlLkVr9+/UReXp7j87a2NpGZmSkSExPF559/rqlJXaZWIeTqZavnydIpBFu9SaZemVqF0GYvt9xoRH19PQIDO69jqtPpsH79esydOxdTpkzBxYsXVaxzJlMrIFcvWz1Plk6Ard4kU69MrYA2e3lVcI0YMWIEsrOzMXLkSKfb165dCwB48MEH1chySaZWQK5etnqeLJ0AW71Jpl6ZWgGN9vp0OxG59etf/1rMnj3b7frChQuFTqfzYZF7MrUKIVcvWz1Plk4h2OpNMvXK1CqENnt5nhsiIiLyK9znhoiIiPwKhxsiIiLyKxxuiIiIyK9wuCEiIiK/wuGGiIiI/AqHGyIiIvIrHG6IiIjIr3C4ISIiIr/yfzuvIc3z85kqAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check out of sample plot\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df_test = pd.read_parquet(\"data/ret-data-cleaned-TEST.parquet\")\n",
    "\n",
    "print(df_test.columns)\n",
    "print(sampled_tickers)\n",
    "df_test_mkt = df_test[mkt_index]\n",
    "\n",
    "r_hat = df_test[sampled_tickers].dot(w_hat)\n",
    "\n",
    "cumret_r = np.cumprod(1+ r_hat)\n",
    "cumret_mkt = np.cumprod(1+ df_test_mkt)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(cumret_mkt.index,\n",
    "        cumret_mkt, \n",
    "       label = mkt_index)\n",
    "\n",
    "ax.plot(cumret_r.index,\n",
    "        cumret_r,\n",
    "       label = f\"ETF ({n_assets} assets)\")\n",
    "\n",
    "ax.legend()\n",
    "ax.set_title(f'ETF and {mkt_index}')\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('Cumulative Returns')\n",
    "\n",
    "plt.xticks(rotation = 90)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  },
  "vscode": {
   "interpreter": {
    "hash": "c9eb7f670fd44fbb412ba40fa7a4438c3fec07df77f89cc007fa432a1163665e"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
